{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T13:30:32Z","timestamp":1772026232892,"version":"3.50.1"},"reference-count":210,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T00:00:00Z","timestamp":1642118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T00:00:00Z","timestamp":1642118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology","doi-asserted-by":"publisher","award":["P0008703"],"award-info":[{"award-number":["P0008703"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["IITP-2021-2018-0-01799"],"award-info":[{"award-number":["IITP-2021-2018-0-01799"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s11227-021-04188-3","type":"journal-article","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T00:03:06Z","timestamp":1642118586000},"page":"8890-8924","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["RETRACTED ARTICLE: Intrusion detection based on machine learning in the internet of things, attacks and counter measures"],"prefix":"10.1007","volume":"78","author":[{"given":"Eid","family":"Rehman","sequence":"first","affiliation":[]},{"given":"Muhammad","family":"Haseeb-ud-Din","sequence":"additional","affiliation":[]},{"given":"Arif Jamal","family":"Malik","sequence":"additional","affiliation":[]},{"given":"Tehmina Karmat","family":"Khan","sequence":"additional","affiliation":[]},{"given":"Aaqif Afzaal","family":"Abbasi","sequence":"additional","affiliation":[]},{"given":"Seifedine","family":"Kadry","sequence":"additional","affiliation":[]},{"given":"Muhammad Attique","family":"Khan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1936-6785","authenticated-orcid":false,"given":"Seungmin","family":"Rho","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,14]]},"reference":[{"key":"4188_CR1","first-page":"34","volume":"3","author":"H Sundmaeker","year":"2010","unstructured":"Sundmaeker H, Guillemin P, Friess P, Woelffl\u00e9 S (2010) Vision and challenges for realising the internet of things. Cluster Eur Res Projects Internet Things Eur Commision 3:34\u201336","journal-title":"Cluster Eur Res Projects Internet Things Eur Commision"},{"key":"4188_CR2","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1016\/j.jnca.2010.04.005","volume":"34","author":"LA Amaral","year":"2011","unstructured":"Amaral LA, Hessel FP, Bezerra EA, Corr\u00eaa JC, Longhi OB, Dias TFO (2011) eCloudRFID\u2013A mobile software framework architecture for pervasive RFID-based applications. J Netw Comput Appl 34:972\u2013979. https:\/\/doi.org\/10.1016\/j.jnca.2010.04.005","journal-title":"J Netw Comput Appl"},{"key":"4188_CR3","unstructured":"Johannes D, Heineke K, Reinbacher T, Wee D (2018) The internet of things: how to capture the value of IoT. Technical Report pp. 1\u2013124"},{"key":"4188_CR4","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1109\/TETC.2016.2606384","volume":"5","author":"A Mosenia","year":"2016","unstructured":"Mosenia A, Jha NK (2016) A comprehensive study of security of Internet-of-Things. IEEE Trans Emerg Topics Comput 5:586\u2013602","journal-title":"IEEE Trans Emerg Topics Comput"},{"key":"4188_CR5","unstructured":"M Young 1989 Checklist before starting the analysis 2 In: The Technical Writer\u2019s Handbook University Science Mill Valley: CA"},{"key":"4188_CR6","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1108\/ICS-07-2018-0084","volume":"27","author":"O Yousuf","year":"2019","unstructured":"Yousuf O, Mir RN (2019) A survey on the internet of things security: State-of-art, architecture, issues and countermeasures. Inf Comput Secur 27:292\u2013323","journal-title":"Inf Comput Secur"},{"key":"4188_CR7","doi-asserted-by":"publisher","unstructured":"Karsligil ME, Yavuz AG, Guvensan MA, Hanifi K, Bank B (2017) Network intrusion detection using machine learning anomaly detection algorithms. In: 25th Signal Processing Commun Applications Conf (SIU), IEEE. https:\/\/doi.org\/10.1109\/siu.2017.7960616","DOI":"10.1109\/siu.2017.7960616"},{"key":"4188_CR8","doi-asserted-by":"crossref","unstructured":"Yang Z, Yue Y, Yang Y, Peng Y, Xiaobo et al. (2011) Study and application on the architecture and key technologies for IoT. In:\u00a02011 Int Conf Multimed Technol, IEEE, pp. 747\u2013751","DOI":"10.1109\/ICMT.2011.6002149"},{"key":"4188_CR9","doi-asserted-by":"publisher","first-page":"2787","DOI":"10.1016\/j.comnet.2010.05.010","volume":"54","author":"L Atzori","year":"2010","unstructured":"Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54:2787\u20132805","journal-title":"Comput Netw"},{"key":"4188_CR10","first-page":"154","volume":"5","author":"A Torkaman","year":"2016","unstructured":"Torkaman A, Seyyedi MA (2016) Analyzing IoT reference architecture models. Int J Comput Sci Softw Eng 5:154","journal-title":"Int J Comput Sci Softw Eng"},{"key":"4188_CR11","doi-asserted-by":"crossref","unstructured":"Chaqfeh MA, Mohamed N (2012) Challenges in middleware solutions for the Internet of Things. In:\u00a02012 Int Conf Collaboration Technol Syst (CTS), pp. 21\u201326, IEEE","DOI":"10.1109\/CTS.2012.6261022"},{"key":"4188_CR12","doi-asserted-by":"crossref","unstructured":"Moustafa N, Creech G, Sitnikova E, Keshk M (2017) Collaborative anomaly detection framework for handling big data of cloud computing. In:\u00a02017 Military Commun Inf Syst Conf (MilCIS), pp. 1\u20136, IEEE","DOI":"10.1109\/MilCIS.2017.8190421"},{"key":"4188_CR13","doi-asserted-by":"publisher","first-page":"1975","DOI":"10.1109\/TIFS.2018.2890808","volume":"14","author":"N Moustafa","year":"2019","unstructured":"Moustafa N, Choo KR, Radwan I, Camtepe S (2019) Outlier dirichlet mixture mechanism: adversarial statistical learning for anomaly detection in the fog. IEEE Trans Inf Foren Secur 14:1975\u20131987","journal-title":"IEEE Trans Inf Foren Secur"},{"key":"4188_CR14","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.comcom.2016.03.007","volume":"89\u201390","author":"F Li","year":"2016","unstructured":"Li F, Han Y, Jin C (2016) Practical access control for sensor networks in the context of the internet of things. Comput Commun 89\u201390:154\u2013164. https:\/\/doi.org\/10.1016\/j.comcom.2016.03.007","journal-title":"Comput Commun"},{"key":"4188_CR15","doi-asserted-by":"publisher","first-page":"178","DOI":"10.3390\/app9010178","volume":"9","author":"B Sudqi Khater","year":"2019","unstructured":"Sudqi Khater B, Wahid A, Idris M, Hussain M, Ibrahim AA (2019) A lightweight perceptron-based intrusion detection system for fog computing. Appl Sci 9:178. https:\/\/doi.org\/10.3390\/app9010178","journal-title":"Appl Sci"},{"key":"4188_CR16","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.comnet.2014.11.008","volume":"76","author":"S Sicari","year":"2015","unstructured":"Sicari S, Rizzardi A, Grieco LA, Cen-Porisini A (2015) Security, privacy and trust in internet of things: The road ahead. Comput Netw 76:146\u2013164","journal-title":"Comput Netw"},{"key":"4188_CR17","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.3390\/electronics9071177","volume":"9","author":"J Asharf","year":"2020","unstructured":"Asharf J, Moustafa N, Khurshid H, Debie E, Haider W, Wahab A (2020) A review of intrusion detection systems using machine and deep learning in internet of things: challenges, solutions and future directions. Electronics 9:1177","journal-title":"Electronics"},{"key":"4188_CR18","doi-asserted-by":"publisher","first-page":"82721","DOI":"10.1109\/ACCESS.2019.2924045","volume":"7","author":"V Hassija","year":"2019","unstructured":"Hassija V, Chamola V, Saxena V, Jain D, Goyal P, Sikdar B (2019) A survey on IoT security: application areas, security threats, and solution architectures. IEEE Access 7:82721\u201382743. https:\/\/doi.org\/10.1109\/ACCESS.2019.2924045","journal-title":"IEEE Access"},{"key":"4188_CR19","doi-asserted-by":"publisher","first-page":"167123","DOI":"10.1109\/ACCESS.2020.3022661","volume":"8","author":"V Sharma","year":"2019","unstructured":"Sharma V, You I, Andersson K, Palmieri F, Rehmani MH, Lim J (2019) Security, privacy and trust for smart mobile-Internet of Things (M-IoT): a survey. IEEE Access 8:167123\u2013167163. https:\/\/doi.org\/10.1109\/ACCESS.2020.3022661","journal-title":"IEEE Access"},{"key":"4188_CR20","doi-asserted-by":"publisher","first-page":"120331","DOI":"10.1109\/ACCESS.2020.3006358","volume":"8","author":"B Liao","year":"2020","unstructured":"Liao B, Ali Y, Nazir S, He L, Khan HU (2020) Security analysis of IoT devices by using mobile computing: a systematic literature review. IEEE Access 8:120331\u2013120350. https:\/\/doi.org\/10.1109\/ACCESS.2020.3006358","journal-title":"IEEE Access"},{"key":"4188_CR21","doi-asserted-by":"publisher","first-page":"151054","DOI":"10.1109\/ACCESS.2019.2947723","volume":"7","author":"T Nandy","year":"2019","unstructured":"Nandy T, Idris MYIB, Md Noor R, Mat Kiah L, Lun LS, Annuar Juma\u2019at NB, Ahmedy I, Abdul Ghani N, Bhattacharyya S, (2019) Review on security of internet of things authentication mechanism. IEEE Access 7:151054\u2013151089. https:\/\/doi.org\/10.1109\/ACCESS.2019.2947723","journal-title":"IEEE Access"},{"key":"4188_CR22","doi-asserted-by":"publisher","first-page":"3441","DOI":"10.1016\/j.comnet.2011.07.001","volume":"55","author":"S Sen","year":"2011","unstructured":"Sen S, Clark JA (2011) Evolutionary computation techniques for intrusion detection in mobile ad hoc networks. Comput Netw 55:3441\u20133457","journal-title":"Comput Netw"},{"key":"4188_CR23","doi-asserted-by":"crossref","unstructured":"Keshk M, Moustafa N, Sitnikova E, Creech G (2017) Privacy preservation intrusion detection technique for SCADA systems. In: 2017 Military Commun Inf Syst Conf (MilCIS) IEEE pp. 1\u20136","DOI":"10.1109\/MilCIS.2017.8190422"},{"key":"4188_CR24","doi-asserted-by":"crossref","unstructured":"Zhao K, Ge L (2013) A survey on the internet of things security. Comput Intell Secur (CIS) 10","DOI":"10.1109\/CIS.2013.145"},{"key":"4188_CR25","first-page":"11","volume":"90","author":"JS Kumar","year":"2014","unstructured":"Kumar JS, Patel DR (2014) A survey on internet of things: security and privacy issues. Int J Comput Appl 90:11","journal-title":"Int J Comput Appl"},{"key":"4188_CR26","doi-asserted-by":"crossref","unstructured":"Suo H, Wan J, Zou JC, Liu J (2012) Security in the internet of things: a review. In: Proc 2012 Int Conf Comput Sci Electron Eng Hangzhou China, 3: 648\u2013651","DOI":"10.1109\/ICCSEE.2012.373"},{"key":"4188_CR27","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.comnet.2018.03.012","volume":"14","author":"DE Kouicem","year":"2018","unstructured":"Kouicem DE, Bouabdallah A, Lakhlef H (2018) Internet of things security: a top-down survey. Comput Netw 14:199\u2013221","journal-title":"Comput Netw"},{"key":"4188_CR28","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.jnca.2017.02.009","volume":"84","author":"BB Zarpel\u00e3o","year":"2017","unstructured":"Zarpel\u00e3o BB, Miani RS, Kawakani CT, Alvarenga SC (2017) A survey of intrusion detection in internet of things. J Netw Comput Appl 84:25\u201337","journal-title":"J Netw Comput Appl"},{"key":"4188_CR29","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.comnet.2019.01.023","volume":"151","author":"KAP Da Costa","year":"2019","unstructured":"Da Costa KAP et al (2019) Internet of things: a survey on machine learning-based intrusion detection approaches. Comput Netw 151:147\u2013157","journal-title":"Comput Netw"},{"key":"4188_CR30","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1109\/MC.2017.201","volume":"50","author":"CG Kolias","year":"2017","unstructured":"Kolias CG, Kambourakis AS, Voas J (2017) DDoS in the IoT: Mirai and other botnets. Computer 50:80\u201384","journal-title":"Computer"},{"key":"4188_CR31","unstructured":"Garadi A, Mohamed MA, Al-Ali A, Du A, Guizani M (2018) A survey of machine and deep learning methods for Internet of Things (IoT) security. arXiv:1807.11023"},{"key":"4188_CR32","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1109\/MSP.2016.4","volume":"14","author":"C Kolias","year":"2016","unstructured":"Kolias C, Stavrou A, Voas J, Bojanova I, Kuhn R (2016) Learning Internet-of-Things security hands-on. IEEE Secur Privacy 14:37\u201346","journal-title":"IEEE Secur Privacy"},{"key":"4188_CR33","doi-asserted-by":"crossref","unstructured":"Marsden T, Moustafa N, Sitnikova E, Creech G (2017) Probability risk identification-based intrusion detection system for SCADA systems. In:\u00a0Int Conf Mobile Netw Manag pp. 353\u2013363","DOI":"10.1007\/978-3-319-90775-8_28"},{"issue":"2","key":"4188_CR34","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1109\/TSUSC.2018.2808430","volume":"6","author":"N Moustafa","year":"2021","unstructured":"Moustafa N, Misra G, Slay J (2021) Generalized outlier gaussian mixture technique based on automated association features for simulating and detecting web application attacks. IEEE Trans Sustain Comput 6(2):245\u2013256. https:\/\/doi.org\/10.1109\/TSUSC.2018.2808430","journal-title":"IEEE Trans Sustain Comput"},{"key":"4188_CR35","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.jnca.2012.05.003","volume":"36","author":"C Modi","year":"2013","unstructured":"Modi C, Patel D, Borisaniya B, Patel H, Patel A, Rajarajan M (2013) A survey of intrusion detection techniques in cloud. J Netw Comput Appl 36:42\u201357","journal-title":"J Netw Comput Appl"},{"key":"4188_CR36","first-page":"11","volume":"2015","author":"R Rizwan","year":"2015","unstructured":"Rizwan R, Khan FA, Abbas H, Chauhdary SH (2015) Anomaly detection in wireless sensor networks using immune-based bioinspired mechanism. Int J Distribut Sens Netw 2015:11\u201310","journal-title":"Int J Distribut Sens Netw"},{"key":"4188_CR37","first-page":"125","volume-title":"Progress in Computing","author":"N Moustafa","year":"2018","unstructured":"Moustafa N, Creech G, Slay J (2018) Anomaly detection system using beta mixture models and outlier detection. Progress in Computing. Springer, Analytics and Networking, pp 125\u2013135"},{"key":"4188_CR38","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/SURV.2013.050113.00191","volume":"16","author":"I Butun","year":"2013","unstructured":"Butun I, Morgera SD, Sankar R (2013) A survey of intrusion detection systems in wireless sensor networks. IEEE Commun Surv Tutor 16:266\u2013282","journal-title":"IEEE Commun Surv Tutor"},{"key":"4188_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2542049","volume":"46","author":"R Mitchell","year":"2014","unstructured":"Mitchell R, Chen I (2014) A survey of intrusion detection techniques for cyber-physical systems. ACM Comput Surv (CSUR) 46:1\u201329","journal-title":"ACM Comput Surv (CSUR)"},{"key":"4188_CR40","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1109\/MWC.2004.1269717","volume":"11","author":"A Mishra","year":"2004","unstructured":"Mishra A, Nadkarni K, Patcha A (2004) Intrusion detection in wireless ad hoc networks. IEEE Wirel Commun 11:48\u201360","journal-title":"IEEE Wirel Commun"},{"key":"4188_CR41","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/s13677-018-0123-6","volume":"7","author":"M Elrawy","year":"2018","unstructured":"Elrawy M, Awad A, Hamed H (2018) Intrusion detection systems for IoT-based smart environments: a survey. J Cloud Comp 7:21. https:\/\/doi.org\/10.1186\/s13677-018-0123-6","journal-title":"J Cloud Comp"},{"key":"4188_CR42","doi-asserted-by":"publisher","first-page":"3496","DOI":"10.1109\/COMST.2018.2844742","volume":"20","author":"E Benkhelifa","year":"2018","unstructured":"Benkhelifa E, Welsh T, Hamouda W (2018) A critical review of practices and challenges in intrusion detection systems for IoT: toward universal and resilient systems. IEEE Commun Surv Tutor 20:3496\u20133509","journal-title":"IEEE Commun Surv Tutor"},{"key":"4188_CR43","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1109\/SURV.2012.121912.00006","volume":"15","author":"A Abduvaliyev","year":"2013","unstructured":"Abduvaliyev A, Pathan AK, Zhou J, Roman R, Wong W (2013) On the vital areas of intrusion detection systems in wireless sensor networks. IEEE Commun Surv Tutor 15:1223\u20131237","journal-title":"IEEE Commun Surv Tutor"},{"key":"4188_CR44","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/978-0-387-33112-6_7","volume-title":"Wireless Network Security","author":"T Anantvalee","year":"2007","unstructured":"Anantvalee T, Wu J (2007) A survey on intrusion detection in mobile ad hoc networks. Wireless Network Security. Springer, Boston, MA, pp 159\u2013180"},{"key":"4188_CR45","doi-asserted-by":"publisher","first-page":"2484","DOI":"10.1002\/sec.1484","volume":"9","author":"S Kumar","year":"2016","unstructured":"Kumar S, Dutta K (2016) Intrusion detection in mobile ad hoc networks: techniques, systems, and future challenges. Secur Commun Netw 9:2484\u20132556","journal-title":"Secur Commun Netw"},{"key":"4188_CR46","doi-asserted-by":"publisher","first-page":"43355","DOI":"10.1109\/ACCESS.2020.2976624","volume":"8","author":"MA Lawal","year":"2020","unstructured":"Lawal MA, Hassan SRA, SR (2020) Security analysis of network anomalies mitigation schemes in IoT networks. IEEE Access 8:43355\u201343374","journal-title":"IEEE Access"},{"key":"4188_CR47","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.future.2019.09.038","volume":"104","author":"S Garg","year":"2020","unstructured":"Garg S, Kaur K, Batra S, Kaddoum G, Kumar N, Boukerche A (2020) A multi-stage anomaly detection scheme for augmenting the security in IoT-enabled applications. Future Generation Comput Syst 104:105\u2013118","journal-title":"Future Generation Comput Syst"},{"key":"4188_CR48","first-page":"24","volume":"16","author":"S Garg","year":"2019","unstructured":"Garg S, Kaur K, Kumar N, Kaddoum G, Zomaya A, Ranjan R (2019) A hybrid deep learning-based model for anomaly detection in cloud datacenter networks. IEEE Trans Netw Service Manag 16:24\u2013935","journal-title":"IEEE Trans Netw Service Manag"},{"key":"4188_CR49","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1109\/JIOT.2014.2358296","volume":"1","author":"S Cirani","year":"2014","unstructured":"Cirani S et al (2014) A scalable and self-configuring architecture for service discovery in the internet of things. IEEE Internet of Things J 1:508\u2013521","journal-title":"IEEE Internet of Things J"},{"key":"4188_CR50","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1109\/ICACTE.2010.5579493","volume":"5","author":"M Wu","year":"2010","unstructured":"Wu M, Lu T-J, Ling F-Y, Sun J, Du H-Y (2010) Research on the architecture of Internet of Things. Int Conf Adv Comput Theory Eng ICACTE 5:484\u2013487. https:\/\/doi.org\/10.1109\/ICACTE.2010.5579493","journal-title":"Int Conf Adv Comput Theory Eng ICACTE"},{"key":"4188_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/9324035","volume":"2017","author":"P Sethi","year":"2017","unstructured":"Sethi P, Sarangi S (2017) Internet of things: architectures, protocols, and applications. J Electric Comput Eng 2017:1\u201325","journal-title":"J Electric Comput Eng"},{"key":"4188_CR52","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06490-w","author":"MA Khan","year":"2021","unstructured":"Khan MA, Muhammad K, Sharif M, Akram T, Kadry S (2021) Intelligent fusion-assisted skin lesion localization and classification for smart healthcare. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-021-06490-w","journal-title":"Neural Comput Appl"},{"key":"4188_CR53","volume-title":"Computer and information security handbook","author":"J Vacca","year":"2013","unstructured":"Vacca J (2013) Computer and information security handbook. Morgan Kaufmann, Amsterdam"},{"issue":"3","key":"4188_CR54","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1109\/TIFS.2018.2866321","volume":"14","author":"J Ning","year":"2018","unstructured":"Ning J, Xu J, Liang K, Zhang F, Chang EC (2018) Passive attacks against searchable encryption. IEEE Trans Inf Forensics Secur 14(3):789\u2013802","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"4188_CR55","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1109\/COMST.2014.2386139","volume":"17","author":"P Faruki","year":"2014","unstructured":"Faruki P, Bharmal A, Laxmi V, Ganmoor V, Gaur MS, Conti M, Rajarajan M (2014) Android security: a survey of issues, malware penetration, and defenses. IEEE Commun Surv Tutor 17:998\u20131022","journal-title":"IEEE Commun Surv Tutor"},{"issue":"4","key":"4188_CR56","doi-asserted-by":"crossref","first-page":"97","DOI":"10.26562\/irjcs.2021.v0804.006","volume":"8","author":"N Hemant","year":"2021","unstructured":"Hemant N, Nihan S, Suresh,  (2021) Survey on cyber attack. IRJCS Int Res J Comput Sci 8(4):97\u2013101","journal-title":"IRJCS Int Res J Comput Sci"},{"key":"4188_CR57","doi-asserted-by":"publisher","first-page":"88892","DOI":"10.1109\/ACCESS.2020.2993553","volume":"8","author":"K Lounis","year":"2020","unstructured":"Lounis K, Zulkernine M (2020) Attacks and defenses in short-range wireless technologies for IoT. IEEE Access 8:88892\u201388932","journal-title":"IEEE Access"},{"key":"4188_CR58","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/I-SMAC.2017.8058363","volume-title":"2017 International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","author":"J Deogirikar","year":"2017","unstructured":"Deogirikar J, Vidhate A (2017) Security attacks in IoT: a survey. In: 2017 International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), IEEE, Palladam, India, pp. 32\u201337. https:\/\/doi.org\/10.1109\/I-SMAC.2017.8058363"},{"key":"4188_CR59","doi-asserted-by":"crossref","unstructured":"Rajan A, Jithish J, Sankaran S (2017) Sybil attack in IoT: modelling and defenses. In:\u00a02017 Int Conf Adv Comput, Commun Informatics (ICACCI) pp. 2323\u20132327","DOI":"10.1109\/ICACCI.2017.8126193"},{"key":"4188_CR60","first-page":"459","volume":"18","author":"A Mayzaud","year":"2016","unstructured":"Mayzaud A, Badonnel R, Chrisment I (2016) A taxonomy of attacks in RPL-based internet of things. Int J Netw Secur 18:459\u2013473","journal-title":"Int J Netw Secur"},{"key":"4188_CR61","doi-asserted-by":"publisher","first-page":"1646","DOI":"10.1109\/COMST.2020.2988293","volume":"22","author":"MA Al-Garadi","year":"2020","unstructured":"Al-Garadi MA, Mohamed A, Al-Ali AK, Du X, Ali I, Guizani M (2020) A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Commun Surv Tutor 22:1646\u20131685","journal-title":"IEEE Commun Surv Tutor"},{"key":"4188_CR62","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3037359","author":"S Khanam","year":"2020","unstructured":"Khanam S, Ahmedy I, Idris M, Jaward M, Sabri A (2020) A survey of security challenges, attacks taxonomy and advanced countermeasures in the internet of things. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2020.3037359","journal-title":"IEEE Access"},{"key":"4188_CR63","doi-asserted-by":"publisher","first-page":"133","DOI":"10.2478\/cait-2019-0008","volume":"19","author":"A Singh","year":"2019","unstructured":"Singh A, Patro B (2019) Security of low computing power devices: a survey of requirements, challenges & possible solutions. Cybern Inf Technol 19:133\u2013164. https:\/\/doi.org\/10.2478\/cait-2019-0008","journal-title":"Cybern Inf Technol"},{"key":"4188_CR64","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/8844910","volume":"2020","author":"L Sun","year":"2020","unstructured":"Sun L, Wang X, Wang J, Liu M, Xia G (2020) RELAP5 foresight thermal-hydraulic analysis of hypothesis passive safety injection system under LOCA for an existing NPP in China. Sci Technol Nuclear Install 2020:1\u201314","journal-title":"Sci Technol Nuclear Install"},{"key":"4188_CR65","doi-asserted-by":"crossref","unstructured":"Heydt-Benjamin TS, Bailey DV, Fu K, Juels A, O\u2019Hare T (2007) Vulnerabilities in first-generation RFID-enabled credit cards. In: Dietrich S, Dhamija R (eds) Financial Cryptography and Data Security. FC 2007. Lecture Notes in Computer Science, p. 4886, Springer, Berlin: Heidelberg","DOI":"10.1007\/978-3-540-77366-5_2"},{"key":"4188_CR66","first-page":"4","volume":"2016","author":"YM Amin","year":"2016","unstructured":"Amin YM, Abdel-Hamid AT (2016) A comprehensive taxonomy and analysis of IEEE 802. 15. 4 attacks. J Electr Comput Eng 2016:4","journal-title":"J Electr Comput Eng"},{"key":"4188_CR67","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1016\/j.csi.2008.09.038","volume":"31","author":"K Bicakci","year":"2009","unstructured":"Bicakci K, Tavli B (2009) Denial-of-service attacks and countermeasures in IEEE 802.11 wireless networks. Comput Stand Interfaces 31:931\u2013941","journal-title":"Comput Stand Interfaces"},{"key":"4188_CR68","doi-asserted-by":"publisher","first-page":"103352","DOI":"10.1016\/j.micpro.2020.103352","volume":"80","author":"A Kumar","year":"2021","unstructured":"Kumar A, Varadarajan V, Kumar A, Dadheech P, Choudhary SS, Kumar VA, Veluvolu KC (2021) Black hole attack detection in vehicular ad-hoc network using secure AODV routing algorithm. Microprocess Microsyst 80:103352","journal-title":"Microprocess Microsyst"},{"key":"4188_CR69","doi-asserted-by":"publisher","first-page":"118","DOI":"10.3390\/s16010118","volume":"16","author":"A Mathur","year":"2016","unstructured":"Mathur A, Newe T, Rao M (2016) Defence against black hole and selective forwarding attacks for medical WSNs in the IoT. Sensors 16:118","journal-title":"Sensors"},{"key":"4188_CR70","doi-asserted-by":"publisher","DOI":"10.1002\/int.22691","author":"M Attique Khan","year":"2021","unstructured":"Attique Khan M, Sharif M, Akram T, Kadry S, Hsu C-H (2021) A two-stream deep neural network-based intelligent system for complex skin cancer types classification. Int J Intell Syst. https:\/\/doi.org\/10.1002\/int.22691","journal-title":"Int J Intell Syst"},{"key":"4188_CR71","first-page":"355","volume":"9","author":"HA Abdul-Ghani","year":"2018","unstructured":"Abdul-Ghani HA, Konstantas D, Mahyoub M (2018) A comprehensive IoT attacks survey based on a building-blocked reference model. Int J Adv Comput Sci Appl 9:355\u2013373","journal-title":"Int J Adv Comput Sci Appl"},{"key":"4188_CR72","unstructured":"Hamza A, Gharakheili HH, Sivaraman V (2020) IoT network security: requirements, threats, and countermeasures. arXiv preprint arXiv:2008.09339"},{"key":"4188_CR73","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5120\/19547-1280","volume":"111","author":"M Farooq","year":"2015","unstructured":"Farooq M, Waseem M, Khairi A, Mazhar P (2015) A critical analysis on the security concerns of internet of things (IoT). Int J Comput Appl 111:1\u20136. https:\/\/doi.org\/10.5120\/19547-1280","journal-title":"Int J Comput Appl"},{"key":"4188_CR74","doi-asserted-by":"crossref","unstructured":"Mitropoulos D, Spinellis D (2017) Fatal injection: A survey of modern code injection attack countermeasures.\u00a0Peer J Comput Sci\u00a03:e136","DOI":"10.7717\/peerj-cs.136"},{"key":"4188_CR75","doi-asserted-by":"publisher","first-page":"3102","DOI":"10.1109\/JIOT.2018.2833206","volume":"5","author":"MN Aman","year":"2018","unstructured":"Aman MN, Sikdar B, Chua KC, Ali A (2018) Low power data integrity in IoT systems. IEEE Internet of Things J 5:3102\u20133113","journal-title":"IEEE Internet of Things J"},{"key":"4188_CR76","doi-asserted-by":"publisher","first-page":"7228","DOI":"10.3390\/app11167228","volume":"11","author":"E Staddon","year":"2021","unstructured":"Staddon E, Loscri V, Mitton N (2021) Attack categorisation for IoT applications in critical infrastructures, a Survey. Appl Sci 11:7228","journal-title":"Appl Sci"},{"key":"4188_CR77","doi-asserted-by":"crossref","unstructured":"Liang X, Kim Y (2021) A survey on security attacks and solutions in the IoT network,\u00a02021 IEEE 11th Ann Comput Commun Workshop Conf (CCWC), pp. 0853\u20130859","DOI":"10.1109\/CCWC51732.2021.9376174"},{"key":"4188_CR78","doi-asserted-by":"publisher","first-page":"31595","DOI":"10.1109\/ACCESS.2021.3059648","volume":"9","author":"TM Hoang","year":"2021","unstructured":"Hoang TM, Duong TQ, Tuan HD, Lambotharan S, Hanzo L (2021) Physical layer security: detection of active eavesdropping attacks by support vector machines. IEEE Access 9:31595\u201331607","journal-title":"IEEE Access"},{"key":"4188_CR79","doi-asserted-by":"publisher","first-page":"146","DOI":"10.3390\/electronics8020146","volume":"8","author":"L Xu","year":"2019","unstructured":"Xu L, Chen J, Liu M, Wang X (2019) Active eavesdropping detection based on large-dimensional random matrix theory for massive MIMO-enabled IoT. Electronics 8:146","journal-title":"Electronics"},{"key":"4188_CR80","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1108\/09685221011079199","volume":"18","author":"A Patel","year":"2010","unstructured":"Patel A, Qassim Q, Wills C (2010) A survey of intrusion detection and prevention systems. Inf Manag Comput Secur 18:277\u2013290","journal-title":"Inf Manag Comput Secur"},{"key":"4188_CR81","unstructured":"Hanif, M. A., Akram, T., Shahzad, A., Tariq, U., Choi, J. I., & Zulfiqar, Z (2021). Smart Devices Based Multisensory Approach for Complex Human Activity Recognition. CMC 1\u201315."},{"key":"4188_CR82","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1109\/MWC.001.2000428","volume":"28","author":"T Alladi","year":"2021","unstructured":"Alladi T et al (2021) Artificial intelligence (AI)-empowered intrusion detection architecture for the internet of vehicles. IEEE Wirel Commun 28:144\u2013149","journal-title":"IEEE Wirel Commun"},{"key":"4188_CR83","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1109\/TC.2013.13","volume":"63","author":"G Creech","year":"2014","unstructured":"Creech G, Hu J (2014) A semantic approach to host-based intrusion detection systems using contiguous and discontiguous system call patterns. IEEE Trans Comput 63:807\u2013819","journal-title":"IEEE Trans Comput"},{"key":"4188_CR84","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1109\/TIE.2010.2052533","volume":"58","author":"F Macia-Perez","year":"2011","unstructured":"Macia-Perez F, Mora-Gimeno FJ, Marcos-Jorquera D, Gil-Martinez-Abarca JA, Ramos-Morillo H, Lorenzo-Fonseca, (2011) Network intrusion detection system embedded on a smart sensor. IEEE Trans Ind Electron 58:722\u2013732","journal-title":"IEEE Trans Ind Electron"},{"key":"4188_CR85","doi-asserted-by":"crossref","unstructured":"Santos L, Gon\u00e7alves R, Rabadao C, Martins J (2021) A flow-based intrusion detection framework for Internet of Things networks.\u00a0Cluster Comput 1\u201321","DOI":"10.1007\/s10586-021-03238-y"},{"key":"4188_CR86","volume-title":"A signature-based intrusion detection system for the internet of things","author":"P Ioulianou","year":"2018","unstructured":"Ioulianou P, Vasilakis V, Moscholios I, Logothetis M (2018) A signature-based intrusion detection system for the internet of things. Paper presented at Information and Communication Technology Form, Graz, Austria"},{"key":"4188_CR87","first-page":"35","volume":"1","author":"V Kumar","year":"2012","unstructured":"Kumar V, Sangwan OP (2012) Signature based intrusion detection system using SNORT. Int J Comput Appl Inf Technol 1:35\u201341","journal-title":"Int J Comput Appl Inf Technol"},{"key":"4188_CR88","doi-asserted-by":"publisher","first-page":"6882","DOI":"10.1109\/JIOT.2020.2970501","volume":"7","author":"M Eskandari","year":"2020","unstructured":"Eskandari M, Janjua ZH, Vecchio M, Antonelli F (2020) Passban IDS: An intelligent anomaly-based intrusion detection system for IoT edge devices. IEEE internet of things J 7:6882\u20136897. https:\/\/doi.org\/10.1109\/JIOT.2020.2970501","journal-title":"IEEE internet of things J"},{"key":"4188_CR89","first-page":"1","volume":"41","author":"AL-H Muna","year":"2018","unstructured":"Muna AL-H, Moustafa N, Sitnikova E (2018) Identification of malicious activities in industrial internet of things based on deep learning models. J Inf Secur Appl 41:1\u201311","journal-title":"J Inf Secur Appl"},{"key":"4188_CR90","unstructured":"Ashraf, A. H., Imran, M., Qahtani, A. M., Alsufyani, A., Almutiry, O., Mahmood, A., & Habib, M (2021). Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s. CMC 1\u201315."},{"key":"4188_CR91","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.pisc.2016.04.005","volume":"8","author":"S Kumar","year":"2016","unstructured":"Kumar S, Gautam OH (2016) Computational neural network regression model for host based intrusion detection system. Perspect Sci 8:93\u201395","journal-title":"Perspect Sci"},{"key":"4188_CR92","doi-asserted-by":"crossref","unstructured":"Moore MR., Bridges RA, Combs FL, Starr MS, Prowell SJ (2017) Modeling inter-signal arrival times for accurate detection of can bus signal injection attacks: a data-driven approach to in-vehicle intrusion detection. In\u00a0Proceedings of the 12th Annual Conference on Cyber and Information Security Research\u00a0(pp. 1\u20134)","DOI":"10.1145\/3064814.3064816"},{"key":"4188_CR93","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1109\/TVT.2019.2961344","volume":"69","author":"H Olufowobi","year":"2019","unstructured":"Olufowobi H, Young C, Zambreno J, Bloom G (2019) Saiducant: Specification-based automotive intrusion detection using controller area network (can) timing. IEEE Trans Vehicular Technol 69:1484\u20131494","journal-title":"IEEE Trans Vehicular Technol"},{"key":"4188_CR94","volume-title":"A pattern matching model for misuse intrusion detection","author":"S Kumar","year":"1996","unstructured":"Kumar S, Spafford EH (1996) A pattern matching model for misuse intrusion detection. The COAST Project, Purdue University"},{"key":"4188_CR95","volume-title":"Technical Report 94-013","author":"S Kumar","year":"1994","unstructured":"Kumar S, Spafford EH (1994) An application of pattern matching in intrusion detection. In: Technical Report 94-013, Dept. of Computer Science, Purdue University"},{"key":"4188_CR96","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/978-981-13-9042-5_24","volume-title":"Computational intelligence in pattern recognition","author":"V Kumar","year":"2020","unstructured":"Kumar V, Das AK, Sinha D (2020) Statistical analysis of the UNSW-NB15 dataset for intrusion detection. Computational intelligence in pattern recognition. Springer, Singapore, pp 279\u2013294"},{"key":"4188_CR97","doi-asserted-by":"publisher","first-page":"5688","DOI":"10.1109\/ACCESS.2017.2770020","volume":"6","author":"A Mehmood","year":"2017","unstructured":"Mehmood A, Khanan A, Umar MM, Abdullah S, Ariffin KAZ, Song H (2017) Secure knowledge and cluster-based intrusion detection mechanism for smart wireless sensor networks. IEEE Access 6:5688\u20135694","journal-title":"IEEE Access"},{"key":"4188_CR98","doi-asserted-by":"publisher","first-page":"102324","DOI":"10.1016\/j.scs.2020.102324","volume":"61","author":"MA Rahman","year":"2020","unstructured":"Rahman MA, Taufiq Asyhari A, Leong LS, Satrya GB, Hai Tao M, Zolkipli MF (2020) Scalable machine learning-based intrusion detection system for IoT-enabled smart cities. Sustain Cities Soc 61:102324","journal-title":"Sustain Cities Soc"},{"key":"4188_CR99","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/SERVICES.2015.12","volume":"2015","author":"MM Hossain","year":"2015","unstructured":"Hossain MM, Fotouhi M, Hasan R (2015) Towards an analysis of security issues, challenges, and open problems in the Internet of Things. Proc IEEE World Congr Services, Jun 2015:21\u201328. https:\/\/doi.org\/10.1109\/SERVICES.2015.12","journal-title":"Proc IEEE World Congr Services, Jun"},{"issue":"1","key":"4188_CR100","first-page":"1","volume":"4","author":"E Alsaadi","year":"2015","unstructured":"Alsaadi E, Tubaishat A (2015) Internet of things: features, challenges, and vulnerabilities. Int J Adv Comput Sci Inform Technol 4(1):1\u201313","journal-title":"Int J Adv Comput Sci Inform Technol"},{"key":"4188_CR101","first-page":"19","volume":"64","author":"U Sabeel","year":"2013","unstructured":"Sabeel U, Chandra N (2013) Categorized security threats in the wireless sensor networks. Countermeas Security Manag Schem 64:19\u201328","journal-title":"Countermeas Security Manag Schem"},{"key":"4188_CR102","doi-asserted-by":"crossref","unstructured":"Hummen R, Hiller J, Wirtz H, Henze M, Shafagh H, Wehrle K (2013) 6LoWPAN fragmentation attacks and mitigation mechanisms. In:\u00a0Proc 6th ACM Conf Secur Privacy Wirel Mobile Netw, pp. 55\u201366","DOI":"10.1145\/2462096.2462107"},{"key":"4188_CR103","volume-title":"Computer and information security handbook","author":"JR Vacca","year":"2012","unstructured":"Vacca JR (2012) Computer and information security handbook. Newnes"},{"key":"4188_CR104","doi-asserted-by":"publisher","first-page":"5110","DOI":"10.1109\/TII.2019.2957140","volume":"16","author":"M Keshk","year":"2019","unstructured":"Keshk M, Turnbull B, Moustafa N, Vatsalan D, Choo KKR (2019) A privacy-preserving-framework-based blockchain and deep learning for protecting smart power networks. IEEE Trans Ind Inform 16:5110\u20135118","journal-title":"IEEE Trans Ind Inform"},{"key":"4188_CR105","first-page":"212","volume":"1","author":"C Liu","year":"2011","unstructured":"Liu C, Yang J, Chen R, Zhang Y, Zeng J (2011) Research on immunity-based intrusion detection technology for the internet of things. Int Conf Natural Comput IEEE 1:212\u2013216","journal-title":"Int Conf Natural Comput IEEE"},{"key":"4188_CR106","doi-asserted-by":"crossref","unstructured":"Kasinathan P, Pastrone C, Spirito MA, Vinkovits M (2013) Denial-of-service detection in 6LoWPAN based internet of things. In:\u00a02013 IEEE 9th Int Conf Wirel Mobile Comput, Netw Commun (WiMob), pp. 600\u2013607","DOI":"10.1109\/WiMOB.2013.6673419"},{"key":"4188_CR107","doi-asserted-by":"crossref","unstructured":"Kasinathan P, Costamagna G, Khaleel H, Pastrone C, Spirito MA (2013) An IDS framework for Internet of Things empowered by 6LoWPAN. In\u00a0Proc 2013 ACM SIGSAC Conf Comput Commun Secur, pp. 1337\u20131340","DOI":"10.1145\/2508859.2512494"},{"key":"4188_CR108","doi-asserted-by":"publisher","first-page":"24188","DOI":"10.3390\/s141224188","volume":"14","author":"D Oh","year":"2014","unstructured":"Oh D, Kim D, Ro WW (2014) A malicious pattern detection engine for embedded security systems in the internet of things. Sensors 14:24188\u201324211","journal-title":"Sensors"},{"key":"4188_CR109","doi-asserted-by":"publisher","DOI":"10.1007\/s11276-018-01912-5","author":"M Keshk","year":"2018","unstructured":"Keshk M, Moustafa N, Turnbull SE, B (2018) Privacy-preserving big data analytics for cyber-physical systems. Wirel Netw. https:\/\/doi.org\/10.1007\/s11276-018-01912-5","journal-title":"Wirel Netw"},{"key":"4188_CR110","unstructured":"Debar H (2000) An introduction to intrusion-detection systems. In:\u00a0Proc Connect\u00a02000"},{"key":"4188_CR111","first-page":"94","volume":"800","author":"K Scarfone","year":"2007","unstructured":"Scarfone K, Mell P (2007) Guide to intrusion detection and prevention systems (IDPS). NIST Spec Publ 800:94","journal-title":"NIST Spec Publ"},{"key":"4188_CR112","doi-asserted-by":"crossref","unstructured":"Amaral JP, Oliveira LM, Rodrigues JJ, Han G, Shu L (2014) Policy and network-based intrusion detection system for IPv6-enabled wireless sensor networks. In:\u00a02014 IEEE Int Conf Commun (ICC), pp. 1796\u20131801","DOI":"10.1109\/ICC.2014.6883583"},{"key":"4188_CR113","doi-asserted-by":"publisher","first-page":"2661","DOI":"10.1016\/j.adhoc.2013.04.014","volume":"11","author":"S Raza","year":"2013","unstructured":"Raza S, Wallgren L, Voigt T (2013) SVELTE: Real-time intrusion detection in the internet of things. Ad hoc Netw 11:2661\u20132674","journal-title":"Ad hoc Netw"},{"key":"4188_CR114","doi-asserted-by":"publisher","first-page":"e3547","DOI":"10.1002\/dac.3547","volume":"31","author":"A Ahmim","year":"2018","unstructured":"Ahmim A, Derdour M, Ferrag MA (2018) An intrusion detection system based on combining probability predictions of a tree of classifiers. Int J Commun Syst 31:e3547","journal-title":"Int J Commun Syst"},{"key":"4188_CR115","doi-asserted-by":"crossref","unstructured":"Setiadi FF, Kesiman MWA, Aryanto KYE (2021) Detection of dos attacks using naive Bayes method based on Internet of Things (IoT). In:\u00a0J Physics: Conf Series 1810:012013. IOP Publishing","DOI":"10.1088\/1742-6596\/1810\/1\/012013"},{"key":"4188_CR116","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.protcy.2012.05.017","volume":"4","author":"S Mukherjee","year":"2017","unstructured":"Mukherjee S, Sharma N (2017) Intrusion detection using naive Bayes classifier with feature reduction. Procedia Technol 4:119\u2013128","journal-title":"Procedia Technol"},{"key":"4188_CR117","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1016\/j.procs.2015.08.220","volume":"60","author":"S Agrawal","year":"2015","unstructured":"Agrawal S, Agrawal J (2015) Survey on anomaly detection using data mining techniques. Procedia Comput Sci 60:708\u2013713","journal-title":"Procedia Comput Sci"},{"key":"4188_CR118","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1016\/j.eswa.2016.07.036","volume":"64","author":"M Swarnkar","year":"2016","unstructured":"Swarnkar M, Hubballi N (2016) OCPAD: One class naive Bayes classifier for payload-based anomaly detection. Expert Syst with Appl 64:330\u2013339","journal-title":"Expert Syst with Appl"},{"key":"4188_CR119","doi-asserted-by":"publisher","first-page":"107542","DOI":"10.1016\/j.compeleceng.2021.107542","volume":"96","author":"S Kanwal","year":"2021","unstructured":"Kanwal S, Shah JH, Khan MA, Nisa M, Kadry S, Sharif M, Maheswari M (2021) Person re-identification using adversarial haze attack and defense: a deep learning framework. Comput Electric Eng 96:107542","journal-title":"Comput Electric Eng"},{"issue":"7","key":"4188_CR120","doi-asserted-by":"publisher","first-page":"1497","DOI":"10.1016\/j.adhoc.2012.02.016","volume":"10","author":"D Miorandi","year":"2012","unstructured":"Miorandi D, Sicari S, De Pellegrini F, Chlamtac I (2012) Internet of things: vision, applications and research challenges. Ad Hoc Netw 10(7):1497","journal-title":"Ad Hoc Netw"},{"key":"4188_CR121","doi-asserted-by":"crossref","unstructured":"Majid, A., Khan, M. A., Nam, Y., Tariq, U., Roy, S., Mostafa, R. R., & Sakr, R. H. (2021). COVID19 classification using CT images via ensembles of deep learning models.\u00a0Computers, Materials and Continua, pp. 319\u2013337.","DOI":"10.32604\/cmc.2021.016816"},{"key":"4188_CR122","doi-asserted-by":"publisher","first-page":"15","DOI":"10.36548\/jtcsst.2020.1.002","volume":"2","author":"SR Mugunthan","year":"2020","unstructured":"Mugunthan SR (2020) Decision tree based interference recognition for fog enabled IoT architecture. J Trends Comput Sci Smart Technol (TCSST) 2:15\u201325","journal-title":"J Trends Comput Sci Smart Technol (TCSST)"},{"key":"4188_CR123","first-page":"1","volume":"14","author":"W Du","year":"2002","unstructured":"Du W, Zhan Z (2002) Building decision tree classifier on private data. Proc IEEE Int Conf Privacy, Secur Data Min, Aus Comput Soc Inc, Sydney, Aus 14:1\u20138","journal-title":"Proc IEEE Int Conf Privacy, Secur Data Min, Aus Comput Soc Inc, Sydney, Aus"},{"key":"4188_CR124","doi-asserted-by":"publisher","first-page":"1210","DOI":"10.3390\/electronics8111210","volume":"8","author":"A Khraisat","year":"2019","unstructured":"Khraisat A, Gondal I, Vamplew P, Kamruzzaman J, Alazab A (2019) A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks. Electronics 8:1210","journal-title":"Electronics"},{"key":"4188_CR125","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/978-981-15-8711-5_11","volume-title":"Multimedia SECURITY","author":"J Manhas","year":"2021","unstructured":"Manhas J, Kotwal S (2021) Implementation of intrusion detection system for internet of things using machine learning techniques. Multimedia SECURITY. Springer, Singapore, pp 217\u2013237"},{"key":"4188_CR126","doi-asserted-by":"crossref","unstructured":"Goeschel K (2016) Reducing false positives in intrusion detection systems using data-mining techniques utilizing support vector machines, decision trees, and naive Bayes for off-line analysis. In:\u00a0SoutheastCon, IEEE, 1\u20136","DOI":"10.1109\/SECON.2016.7506774"},{"key":"4188_CR127","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/240217","author":"W Li","year":"2014","unstructured":"Li W, Yi P, Wu Y, Pan L, Li J (2014) A new intrusion detection system based on KNN classification algorithm in wireless sensor network. J Electric Comput Eng. https:\/\/doi.org\/10.1155\/2014\/240217","journal-title":"J Electric Comput Eng"},{"key":"4188_CR128","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/8814141","author":"G Farahani","year":"2021","unstructured":"Farahani G (2021) Black hole attack detection using K-nearest neighbor algorithm and reputation calculation in mobile ad hoc networks. Secur Commun Netw. https:\/\/doi.org\/10.1155\/2021\/8814141","journal-title":"Secur Commun Netw"},{"key":"4188_CR129","doi-asserted-by":"publisher","first-page":"1206","DOI":"10.3390\/electronics9081206","volume":"9","author":"H Xu","year":"2020","unstructured":"Xu H, Przystupa K, Fang C, Marciniak A, Kochan O, Beshley M (2020) A combination strategy of feature selection based on an integrated optimization algorithm and weighted K-nearest neighbor to improve the performance of network intrusion detection. Electronics 9:1206","journal-title":"Electronics"},{"key":"4188_CR130","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1109\/TETC.2016.2633228","volume":"7","author":"HH Pajouh","year":"2016","unstructured":"Pajouh HH, Javidan R, Khayami R, Dehghantanha A, Choo K-KR (2016) A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks. IEEE Trans Emerg Topics Comput 7:314\u2013323","journal-title":"IEEE Trans Emerg Topics Comput"},{"key":"4188_CR131","first-page":"45","volume":"2","author":"S Tong","year":"2001","unstructured":"Tong S, Koller D (2001) Support vector machine active learning with applications to text classification. J Mach Learn Res 2:45\u201366","journal-title":"J Mach Learn Res"},{"key":"4188_CR132","doi-asserted-by":"crossref","unstructured":"Jing D, Chen H (2019) SVM based network intrusion detection for the UNSW-NB15 dataset.\u00a02019 IEEE 13th Int Conf ASIC (ASICON), pp. 1\u20134","DOI":"10.1109\/ASICON47005.2019.8983598"},{"key":"4188_CR133","unstructured":"Rehman, M. U., Ahmed, F., Khan, M. A., Tariq, U., Alfouzan, F. A., Alzahrani, N. M., & Ahmad, J (2021). Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks. CMC pp. 1\u201315."},{"key":"4188_CR134","first-page":"4043","volume":"11","author":"Y Liu","year":"2017","unstructured":"Liu Y, Pi D (2017) A novel kernel SVM algorithm with game theory for network intrusion detection. KSII Trans Internet Inf Syst 11:4043","journal-title":"KSII Trans Internet Inf Syst"},{"key":"4188_CR135","unstructured":"Hu W, Liao Y, Vemuri VR (2003) Robust support vector machines for anomaly detection in computer security. In:\u00a0ICMLA pp. 168\u2013174"},{"key":"4188_CR136","first-page":"28","volume-title":"Int Conf Res Networking","author":"C Wagner","year":"2011","unstructured":"Wagner C, Fran\u00e7ois J, Engel T (2011) Machine learning approach for IP-flow record anomaly detection. Int Conf Res Networking. Springer, Berlin, Heidelberg, pp 28\u201339"},{"key":"4188_CR137","doi-asserted-by":"crossref","unstructured":"Garg S, Kaur K, Kaddoum G, Gagnon F, Kumar N, Han Z (2019) Sec-IoV: A multi-stage anomaly detection scheme for Internet of vehicles, In:\u00a0Proc ACM MobiHoc Workshop Pervasive Syst IoT Era, pp. 37\u201342","DOI":"10.1145\/3331052.3332476"},{"key":"4188_CR138","doi-asserted-by":"publisher","first-page":"2823","DOI":"10.1007\/s13042-018-00906-1","volume":"10","author":"JM Torres","year":"2019","unstructured":"Torres JM, Comesa\u00f1a CI, Garcia-Nieto PJ (2019) Machine learning techniques applied to cybersecurity. Int J Mach Learn Cybernet 10:2823\u20132836","journal-title":"Int J Mach Learn Cybernet"},{"key":"4188_CR139","doi-asserted-by":"crossref","unstructured":"Ioannou C, Vassiliou V (2019) Classifying security attacks in IoT networks using supervised learning. In:\u00a02019 15th Int Conf Distributed Comput Sensor Syst, IEEE, pp. 652\u2013658","DOI":"10.1109\/DCOSS.2019.00118"},{"key":"4188_CR140","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/986428","author":"K-C Lin","year":"2014","unstructured":"Lin K-C, Chen S, Hung JC (2014) Botnet detection using support vector machines with artificial fish swarm algorithm. J Appl Math. https:\/\/doi.org\/10.1155\/2014\/986428","journal-title":"J Appl Math"},{"key":"4188_CR141","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learning 45:5\u201332","journal-title":"Mach Learning"},{"key":"4188_CR142","doi-asserted-by":"publisher","first-page":"1976","DOI":"10.3390\/s17091967","volume":"17","author":"M Lopez-Martin","year":"2017","unstructured":"Lopez-Martin M, Carro B, Sanchez-Esguevillas A, Lloret J (2017) Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in IoT. Sensors 17:1976","journal-title":"Sensors"},{"key":"4188_CR143","doi-asserted-by":"crossref","unstructured":"Chen, Y., Tao, J., Zhang, Q., Yang, K., Chen, X., Xiong, J., & Xie, J. (2020). Saliency detection via the improved hierarchical principal component analysis method.\u00a0Wireless communications and mobile computing,\u00a02020.","DOI":"10.1155\/2020\/8822777"},{"key":"4188_CR144","first-page":"1801","volume":"69","author":"N Islam","year":"2021","unstructured":"Islam N, Farhin F, Sultana I, Kaiser MS, Rahman MS, Mahmud M, Sanwar Hosen ASM, Cho GH (2021) Towards machine learning based intrusion detection in IoT networks. Comput Mater Contin 69:1801\u20131821","journal-title":"Comput Mater Contin"},{"key":"4188_CR145","doi-asserted-by":"crossref","unstructured":"Zhang J, Zulkernine M (2006) A hybrid network intrusion detection technique using random forests, In\u00a0First Int Conf on Availability, Reliability Secur (ARES '06), IEEE, p. 8","DOI":"10.1109\/ARES.2006.7"},{"key":"4188_CR146","doi-asserted-by":"crossref","unstructured":"Doshi R, Apthorpe N, Feamster N (2018) Machine learning DDoS detection for consumer Internet of Things devices. In:\u00a02018 IEEE Secur Privacy Workshops (SPW), pp. 29\u201335","DOI":"10.1109\/SPW.2018.00013"},{"key":"4188_CR147","unstructured":"Meidan Y, Bohadana M, Shabtai A, Ochoa M, Tippenhauer NO, et al. (2017) Detection of unauthorized IoT devices using machine learning techniques. arXiv preprint arXiv, 1709.04647"},{"key":"4188_CR148","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.inffus.2013.04.006","volume":"16","author":"M Wo\u017aniak","year":"2014","unstructured":"Wo\u017aniak M, Grana M, Corchado E (2014) A survey of multiple classifier systems as hybrid systems. Inf Fusion 16:3\u201317","journal-title":"Inf Fusion"},{"key":"4188_CR149","doi-asserted-by":"crossref","unstructured":"Illy P, Kaddoum G, Moreira CM, Kaur K, Garg S (2019) Securing fog-to-things environment using intrusion detection system based on ensemble learning. In:\u00a02019 IEEE Wirel Commun Netw Conf (WCNC), pp. 1\u20137","DOI":"10.1109\/WCNC.2019.8885534"},{"key":"4188_CR150","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1145\/2347736.2347755","volume":"55","author":"P Domingos","year":"2012","unstructured":"Domingos P (2012) A few useful things to know about machine learning. Commun ACM 55:78\u201387","journal-title":"Commun ACM"},{"key":"4188_CR151","doi-asserted-by":"crossref","unstructured":"Zhang H, Liu D, Luo Y, Wang D (2012) Adaptive dynamic programming for control: algorithms and stability. Springer Sci Bus Media","DOI":"10.1007\/978-1-4471-4757-2"},{"key":"4188_CR152","first-page":"266","volume":"81","author":"MN Baba","year":"2015","unstructured":"Baba MN, Makhtar M, Fadzli SA, Awang MK (2015) Current issues in ensemble methods and its applications. J Theoretical Appl Technol 81:266","journal-title":"J Theoretical Appl Technol"},{"key":"4188_CR153","doi-asserted-by":"crossref","unstructured":"Santana L, Silva L, Canuto AMP, Pintro F, Vale KMO (2010) A comparative analysis of genetic algorithm and ant colony optimization to select attributes for an heterogeneous ensemble of classifiers. In:\u00a0IEEE Congress Evolutionary Comput, pp. 1\u20138","DOI":"10.1109\/CEC.2010.5586080"},{"key":"4188_CR154","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.asoc.2015.10.011","volume":"38","author":"AA Aburomman","year":"2016","unstructured":"Aburomman AA, Reaz MBI (2016) A novel SVM-kNN-PSO ensemble method for intrusion detection system. Appl Soft Comput 38:360\u2013372","journal-title":"Appl Soft Comput"},{"key":"4188_CR155","doi-asserted-by":"crossref","unstructured":"Gaikwad DP, Thool RC (2015) Intrusion detection system using bagging ensemble method of machine learning. In:\u00a02015 Int Conf Comput Commun Control Automation, IEEE, pp. 291\u2013295","DOI":"10.1109\/ICCUBEA.2015.61"},{"key":"4188_CR156","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.23906","author":"M Irshad","year":"2021","unstructured":"Irshad M, Sharif M, Yasmin M, Rehman A, Khan MA (2021) Discrete light sheet microscopic segmentation of left ventricle using morphological tuning and active contours. Microscopy Res Tech. https:\/\/doi.org\/10.1002\/jemt.23906","journal-title":"Microscopy Res Tech"},{"key":"4188_CR157","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.adhoc.2015.07.013","volume":"35","author":"HHWJ Bosman","year":"2015","unstructured":"Bosman HHWJ, Iacca G, Tejada A, W\u00f6rtche HF, Liotta A (2015) Ensembles of incremental learners to detect anomalies in ad hoc sensor networks. Ad Hoc Netw 35:14\u201336","journal-title":"Ad Hoc Netw"},{"key":"4188_CR158","doi-asserted-by":"publisher","first-page":"34","DOI":"10.22967\/HCIS.2021.11.034","volume":"11","author":"N Hussain","year":"2021","unstructured":"Hussain N, Khan MA, Kadry S, Tariq U, Mostafa RR, Choi JI, Nam Y (2021) Intelligent deep learning and improved whale optimization algorithm based framework for object recognition. Hum Cent Comput Inf Sci 11:34","journal-title":"Hum Cent Comput Inf Sci"},{"key":"4188_CR159","first-page":"100","volume":"28","author":"J Hartigan","year":"1979","unstructured":"Hartigan J, Wong MA (1979) AK-means clustering algorithm. J Royal Stat Soc: Series C Appl Stat 28:100\u2013108","journal-title":"J Royal Stat Soc: Series C Appl Stat"},{"key":"4188_CR160","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1109\/SURV.2013.052213.00046","volume":"16","author":"M Bhuyan","year":"2013","unstructured":"Bhuyan M, Bhattacharyya DK, Kalita JK (2013) Network anomaly detection: methods, systems and tools. IEEE Commun Surv Tutor 16:303\u2013336","journal-title":"IEEE Commun Surv Tutor"},{"key":"4188_CR161","doi-asserted-by":"publisher","first-page":"569","DOI":"10.18178\/ijmlc.2019.9.5.841","volume":"9","author":"S Kanjanawattana","year":"2019","unstructured":"Kanjanawattana S (2019) A novel outlier detection applied to an adaptive k-means. Int J Mach Learning Comput 9:569\u2013574","journal-title":"Int J Mach Learning Comput"},{"key":"4188_CR162","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.proeng.2012.01.849","volume":"30","author":"AP Muniyandi","year":"2012","unstructured":"Muniyandi AP, Rajeswari R, Rajaram R (2012) Network anomaly detection by cascading k-Means clustering and C4. 5 decision tree algorithms. Procedia Eng 30:174\u2013182","journal-title":"Procedia Eng"},{"key":"4188_CR163","doi-asserted-by":"crossref","unstructured":"Alharbi S, Rodriguez P, Maharaja R, Iyer P, Subaschandrabose N, Ye Z (2017) Secure the Internet of Things with challenge response authentication in fog computing. In: 2017 IEEE 36th Int Performance Comput Commun Conf (IPCCC), pp. 1\u20132","DOI":"10.1109\/PCCC.2017.8280489"},{"key":"4188_CR164","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1109\/COMST.2016.2627399","volume":"19","author":"MH Cintuglu","year":"2016","unstructured":"Cintuglu MH, Mohammed OA, Akkaya K, Uluagac AS (2016) A survey on smart grid cyber-physical system testbeds. IEEE Commun Surv Tutor 19:446","journal-title":"IEEE Commun Surv Tutor"},{"key":"4188_CR165","doi-asserted-by":"crossref","unstructured":"Sellappan D, Srinivasan R (2020) Association rule-mining-based intrusion detection system with entropy-based feature selection: Intrusion detection system. In\u00a0Handbook of Research on Intelligent Data Processing and Information Security Systems, IGI Global, pp. 1\u201324","DOI":"10.4018\/978-1-7998-1290-6.ch001"},{"key":"4188_CR166","first-page":"347","volume":"2","author":"V Markam","year":"2012","unstructured":"Markam V, Dubey LSM (2012) A general study of associations rule mining in intrusion detection system. Int J Emerg Technol Adv Eng 2:347\u2013356","journal-title":"Int J Emerg Technol Adv Eng"},{"key":"4188_CR167","doi-asserted-by":"crossref","unstructured":"Manimurugan S (2021) IoT-fog-cloud model for anomaly detection using improved na\u00efve Bayes and principal component analysis.\u00a0J Ambient Intell Humanized Comput, pp. 1\u201310","DOI":"10.1007\/s12652-020-02723-3"},{"key":"4188_CR168","doi-asserted-by":"crossref","unstructured":"Zhao S, Li W, Zia T, Zomaya AY (2017) A dimension reduction model and classifier for anomaly-based intrusion detection in Internet of Things. In:\u00a02017 IEEE 15th Intl Conf Dependable, Autonomic Secure Comput, 15th Intl Conf Pervasive Intell Comput, 3rd Intl Conf Big Data Intell Comput Cyber Science and Technol Congress (DASC\/PiCom\/DataCom\/CyberSciTech), pp. 836\u2013843","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2017.141"},{"key":"4188_CR169","doi-asserted-by":"crossref","unstructured":"Hoang DH, Nguyen HD (2019) Detecting anomalous network traffic in IoT networks, In\u00a02019 21st Int Conf Adv Commun Technol (ICACT), pp. 1143\u20131152","DOI":"10.23919\/ICACT.2019.8702032"},{"key":"4188_CR170","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.procs.2016.07.345","volume":"92","author":"J Hussain","year":"2016","unstructured":"Hussain J, Lalmuanawma S (2016) Feature analysis, evaluation and comparisons of classification algorithms based on noisy intrusion dataset. Procedia Comput Sci 92:188\u2013198","journal-title":"Procedia Comput Sci"},{"key":"4188_CR171","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.ins.2016.04.019","volume":"378","author":"RAR Ashfaq","year":"2017","unstructured":"Ashfaq RAR, Wang X, Huang JZ, Abbas H, He Y (2017) Fuzziness based semi-supervised learning approach for intrusion detection system. Inf Sci 378:484\u2013497","journal-title":"Inf Sci"},{"key":"4188_CR172","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.neunet.2017.01.012","volume":"92","author":"MRG Raman","year":"2017","unstructured":"Raman MRG, Somu N, Kirthivasan K, Sriram VSS (2017) A hypergraph and arithmetic residue-based probabilistic neural network for classification in intrusion detection systems. Neural Netw 92:89\u201397","journal-title":"Neural Netw"},{"key":"4188_CR173","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1145\/382912.382923","volume":"3","author":"J McHugh","year":"2000","unstructured":"McHugh J (2000) Testing intrusion detection systems: A critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln laboratory. ACM Trans Inf Syst Secur (TISSEC) 3:262\u2013294","journal-title":"ACM Trans Inf Syst Secur (TISSEC)"},{"key":"4188_CR174","doi-asserted-by":"crossref","unstructured":"Moustafa N, Slay J (2015) UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In:\u00a02015 Military Commun Inf Syst Conf (MilCIS), pp. 1\u20136. IEEE","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"4188_CR175","first-page":"177","volume":"1","author":"I Sharafaldin","year":"2018","unstructured":"Sharafaldin I, Gharib A, Lashkari AH, Ghorbani AA (2018) Towards a reliable intrusion detection benchmark dataset. Softw Netw 1:177\u2013200","journal-title":"Softw Netw"},{"key":"4188_CR176","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.cose.2011.12.012","volume":"31","author":"A Shiravi","year":"2012","unstructured":"Shiravi A, Shiravi H, Tavallaee M, Ghorbani AA (2012) Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput Secur 31:357\u2013374","journal-title":"Comput Secur"},{"key":"4188_CR177","first-page":"111","volume-title":"Int Conf Inf Secur Digital Forensics","author":"JO Nehinbe","year":"2009","unstructured":"Nehinbe JO (2009) A simple method for improving intrusion detections in corporate networks. Int Conf Inf Secur Digital Forensics. Springer, Berlin, Germany, pp 111\u2013122"},{"key":"4188_CR178","first-page":"172","volume-title":"Int Conf Inf Syst Security Privacy","author":"I Sharafaldin","year":"2018","unstructured":"Sharafaldin I, Lashkari AH, Ghorbani AA (2018) A detailed analysis of the CICIDS2017 data set. Int Conf Inf Syst Security Privacy. Springer, Cham, pp 172\u2013188"},{"key":"4188_CR179","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/j.future.2019.05.041","volume":"100","author":"N Koroniotis","year":"2019","unstructured":"Koroniotis N, Moustafa N, Sitnikova E, Turnbull B (2019) Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gen Comput Syst 100:779\u2013796","journal-title":"Future Gen Comput Syst"},{"key":"4188_CR180","unstructured":"Pahl M-O, Aubet F-X (2018) DS2OS traffic traces: IoT traffic traces gathered in a the DS2OS IoT environment. https:\/\/www.kaggle.com\/francoisxa\/ds2ostraffictraces."},{"key":"4188_CR181","unstructured":"Pahl M-O, Aubet F-X (2018) All eyes on you: Distributed multi-dimensional IoT microservice anomaly detection. In: 2018 14th Int Conf Netw Service Manag (CNSM), pp. 72\u201380. IEEE"},{"key":"4188_CR182","doi-asserted-by":"crossref","unstructured":"Masduki BW, Ramli K, Saputra FA, Sugiarto D (2015) Study on implementation of machine learning methods combination for improving attacks detection accuracy on Intrusion Detection System (IDS). In:\u00a02015 Int Conf Quality Res (QiR), IEEE, pp. 56\u201364","DOI":"10.1109\/QiR.2015.7374895"},{"key":"4188_CR183","first-page":"683","volume":"17","author":"M Bhuyan","year":"2015","unstructured":"Bhuyan M, Bhattacharyya DK, Kalita JK (2015) Towards generating real-life datasets for network intrusion detection. Int J Netw Secur 17:683\u2013701","journal-title":"Int J Netw Secur"},{"key":"4188_CR184","doi-asserted-by":"crossref","unstructured":"Sharafaldin I, Lashkari AH, Ghorbani AA (2018) Toward generating a new intrusion detection dataset and intrusion traffic characterization. In:\u00a0ICISSp, pp. 108\u2013116","DOI":"10.5220\/0006639801080116"},{"key":"4188_CR185","unstructured":"Hindy H, Brosset D, Bayne E, Seeam A, Tachtatzis C, Atkinson R, Bellekens X (2018) A taxonomy and survey of intrusion detection system design techniques, network threats and datasets."},{"key":"4188_CR186","unstructured":"Singh G, Khare N (2021) A survey of intrusion detection from the perspective of intrusion datasets and machine learning techniques.\u00a0Int J Comput Appl pp. 1\u201311"},{"key":"4188_CR187","first-page":"102532","volume":"53","author":"GS Kushwah","year":"2020","unstructured":"Kushwah GS, Ranga V (2020) Voting extreme learning machine based distributed denial of service attack detection in cloud computing. J Inform Security Appl 53:102532","journal-title":"J Inform Security Appl"},{"key":"4188_CR188","doi-asserted-by":"crossref","unstructured":"Sultan S, Javaid Q, Malik AJ, Al-Turjman F, Attique M (2021) Collaborative-trust approach toward malicious node detection in vehicular ad hoc networks.\u00a0Environ, Develop Sustain, pp. 1\u201319","DOI":"10.1007\/s10668-021-01632-5"},{"key":"4188_CR189","doi-asserted-by":"publisher","first-page":"102676","DOI":"10.1016\/j.ipm.2021.102676","volume":"58","author":"S Kanwal","year":"2021","unstructured":"Kanwal S, Iqbal Z, Al-Turjman F, Irtaza A, Khan MA (2021) Multiphase fault tolerance genetic algorithm for VM and task scheduling in datacenter. Inf Process Manag 58:102676","journal-title":"Inf Process Manag"},{"key":"4188_CR190","doi-asserted-by":"publisher","first-page":"102675","DOI":"10.1016\/j.ipm.2021.102675","volume":"58","author":"GJ Ansari","year":"2021","unstructured":"Ansari GJ, Shah JH, Sharif M, Tariq U, Akram T (2021) A non-blind deconvolution semi pipelined approach to understand text in blurry natural images for edge intelligence. Inf Process Manag 58:102675","journal-title":"Inf Process Manag"},{"key":"4188_CR191","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/s10845-020-01722-7","volume":"32","author":"A Sharif","year":"2021","unstructured":"Sharif A, Li JP, Saleem MA, Manogran G, Kadry S, Basit A, Khan MA (2021) A dynamic clustering technique based on deep reinforcement learning for internet of vehicles. J Intell Manuf 32:757\u2013768","journal-title":"J Intell Manuf"},{"key":"4188_CR192","doi-asserted-by":"crossref","unstructured":"Almadhor A, Rauf HT, Khan MA, Kadry S, Nam Y (2021) A hybrid algorithm (BAPSO) for capacity configuration optimization in a distributed solar PV based microgrid. Energ Rep, ISSN 2352\u20134847","DOI":"10.1016\/j.egyr.2021.01.034"},{"key":"4188_CR193","doi-asserted-by":"publisher","first-page":"533","DOI":"10.32604\/cmes.2021.014355","volume":"127","author":"R Saeed","year":"2021","unstructured":"Saeed R, Rubab S, Asif S, Khan MM, Murtaza S, Kadry S, Nam Y, Khan MA (2021) An automated system to predict popular cybersecurity news using document embeddings. Comput Modeling Eng Sci 127:533\u2013547","journal-title":"Comput Modeling Eng Sci"},{"key":"4188_CR194","doi-asserted-by":"crossref","unstructured":"Zhang Y-D, Alhusseni M, Kadry S, Wang S-H, Saba T, Iqbal T (2021) A fused heterogeneous deep neural network and robust feature selection framework for human actions recognition.\u00a0Arabian J Sci Eng pp. 1\u201316","DOI":"10.1007\/s13369-021-06510-w"},{"key":"4188_CR195","doi-asserted-by":"publisher","first-page":"2217","DOI":"10.32604\/cmc.2021.018103","volume":"69","author":"M Ahmed","year":"2021","unstructured":"Ahmed M, Ramzan M, Khan HU, Iqbal S, Choi J-I, Nam Y, Kadry S (2021) Real-time violent action recognition using key frames extraction and deep learning. CMC-Comput Mater Continua 69:2217\u20132230","journal-title":"CMC-Comput Mater Continua"},{"key":"4188_CR196","doi-asserted-by":"crossref","unstructured":"Nasir IM, Raza M, Shah JH, Khan MA, Rehman A (2021) Human action recognition Using machine learning in uncontrolled environment. In:\u00a02021 1st Int Conf Artif Intell Data Analyt (CAIDA), IEEE, pp. 182\u2013187","DOI":"10.1109\/CAIDA51941.2021.9425202"},{"key":"4188_CR197","doi-asserted-by":"publisher","first-page":"4061","DOI":"10.32604\/cmc.2021.017800","volume":"69","author":"S Kiran","year":"2021","unstructured":"Kiran S, Javed MY, Alhaisoni M, Tariq U, Nam Y, Dama\u0161evicius R, Sharif M (2021) Multi-layered deep learning features fusion for human action recognition. CMC-Comput Mater Continua 69:4061\u20134075","journal-title":"CMC-Comput Mater Continua"},{"key":"4188_CR198","doi-asserted-by":"publisher","first-page":"3841","DOI":"10.32604\/cmc.2021.016864","volume":"68","author":"MA Khan","year":"2021","unstructured":"Khan MA, Alhaisoni M, Armghan A, Alenezi F, Tariq U, Nam Y, Akram T (2021) Video analytics framework for human action recognition. CMC-Comput Mater Continua 68:3841\u20133859","journal-title":"CMC-Comput Mater Continua"},{"key":"4188_CR199","doi-asserted-by":"crossref","unstructured":"Zhang Y-D, Khan SA, Attique M, Rehman A, Seo S (2020) A resource conscious human action recognition framework using 26-layered deep convolutional neural network.\u00a0Multimed Tools Appl, pp. 1\u201323","DOI":"10.1007\/s11042-020-09408-1"},{"key":"4188_CR200","doi-asserted-by":"crossref","unstructured":"Javed K, Khan SA, Saba T, Habib U, Khan JA, Abbasi AA (2020) Human action recognition using fusion of multiview and deep features: an application to video surveillance.\u00a0Multimed Tools Applications, pp. 1\u201327","DOI":"10.1007\/s11042-020-08806-9"},{"key":"4188_CR201","first-page":"43","volume":"22","author":"UN Hussain","year":"2020","unstructured":"Hussain UN, Lali IU, Javed K, Ashraf I, Tariq J, Ali H, Din A (2020) A unified design of ACO and skewness based brain tumor segmentation and classification from MRI scans. J Control Eng Appl Inform 22:43\u201355","journal-title":"J Control Eng Appl Inform"},{"key":"4188_CR202","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.patrec.2019.11.034","volume":"129","author":"M Sharif","year":"2020","unstructured":"Sharif M, Akram T, Bukhari SAC, Nayak RS (2020) Developed Newton-Raphson based deep features selection framework for skin lesion recognition. Pattern Recognit Lett 129:293\u2013303","journal-title":"Pattern Recognit Lett"},{"key":"4188_CR203","doi-asserted-by":"publisher","first-page":"811","DOI":"10.3390\/diagnostics11050811","volume":"11","author":"M Sharif","year":"2021","unstructured":"Sharif M, Akram T, Dama\u0161evi\u010dius R, Maskeli\u016bnas R (2021) Skin lesion segmentation and multiclass classification using deep learning features and improved moth flame optimization. Diagnostics 11:811","journal-title":"Diagnostics"},{"key":"4188_CR204","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3067789","author":"MA Khan","year":"2021","unstructured":"Khan MA, Khan M, Sharif M, Akram T, de AlbuquerqueVC C (2021) Multi-class skin lesion detection and classification via teledermatology. IEEE J Biomed Health Inform. https:\/\/doi.org\/10.1109\/JBHI.2021.3067789","journal-title":"IEEE J Biomed Health Inform"},{"key":"4188_CR205","doi-asserted-by":"publisher","first-page":"106956","DOI":"10.1016\/j.compeleceng.2020.106956","volume":"90","author":"Y-D Zhang","year":"2021","unstructured":"Zhang Y-D, Sharif M, Akram T (2021) Pixels to classes: intelligent learning framework for multiclass skin lesion localization and classification. Comput Electric Eng 90:106956","journal-title":"Comput Electric Eng"},{"key":"4188_CR206","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1002\/jemt.23578","volume":"84","author":"M Qasim","year":"2021","unstructured":"Qasim M, Lodhi HMJ, Nazir M, Javed K, Rubab S, Din A, Habib U (2021) Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM. Microscopy Res Tech 84:202\u2013216","journal-title":"Microscopy Res Tech"},{"key":"4188_CR207","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.32604\/cmc.2021.015154","volume":"68","author":"ABT Tahir","year":"2021","unstructured":"Tahir ABT, Alhaisoni M, Khan JA, Nam Y, Wang S-H, Javed K (2021) Deep learning and improved particle swarm optimization based multimodal brain tumor classification. CMC-Comput Mater Continua 68:1099\u20131116","journal-title":"CMC-Comput Mater Continua"},{"key":"4188_CR208","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.32604\/cmc.2021.016307","volume":"68","author":"T Akram","year":"2021","unstructured":"Akram T, Sharif M, Kadry S, Nam Y (2021) Computer decision support system for skin cancer localization and classification. CMC-Comput Mater Continua 68:1041\u20131064","journal-title":"CMC-Comput Mater Continua"},{"key":"4188_CR209","doi-asserted-by":"crossref","unstructured":"Mehmood A, Sharif M, Khan SA, Shaheen M, Saba T, Riaz N, Ashraf I (2020) Prosperous human gait recognition: an end-to-end system based on pre-trained CNN features selection.\u00a0Multimed Tools Appl, pp. 1\u201321","DOI":"10.1007\/s11042-020-08928-0"},{"key":"4188_CR210","doi-asserted-by":"crossref","unstructured":"Hussain N, Sharif M, Khan SA, Albesher AA, Saba T, Armaghan A. (2020) A deep neural network and classical features based scheme for objects recognition: an application for machine inspection.\u00a0Multimed Tools Appl,pp. 1\u201323","DOI":"10.1007\/s11042-020-08852-3"}],"updated-by":[{"DOI":"10.1007\/s11227-024-05973-6","type":"retraction","label":"Retraction","source":"retraction-watch","updated":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T00:00:00Z","timestamp":1707782400000},"record-id":"52853"},{"DOI":"10.1007\/s11227-024-05973-6","type":"retraction","label":"Retraction","source":"publisher","updated":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T00:00:00Z","timestamp":1707782400000}}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-021-04188-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-021-04188-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-021-04188-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T15:21:33Z","timestamp":1708183293000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-021-04188-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,14]]},"references-count":210,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["4188"],"URL":"https:\/\/doi.org\/10.1007\/s11227-021-04188-3","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,14]]},"assertion":[{"value":"2 November 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2024","order":3,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":4,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This article has been retracted. Please see the Retraction Notice for more detail:","order":5,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s11227-024-05973-6","URL":"https:\/\/doi.org\/10.1007\/s11227-024-05973-6","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}}]}}