{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T06:31:40Z","timestamp":1782887500881,"version":"3.54.5"},"reference-count":82,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Peer-to-Peer Netw. Appl."],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s12083-024-01650-w","type":"journal-article","created":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T11:03:11Z","timestamp":1708081391000},"page":"1237-1262","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Intrusion detection system extended CNN and artificial bee colony optimization in wireless sensor networks"],"prefix":"10.1007","volume":"17","author":[{"given":"K.","family":"Yesodha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Krishnamurthy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Selvi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Kannan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"issue":"2","key":"1650_CR1","doi-asserted-by":"crossref","first-page":"145","DOI":"10.4018\/IJISP.2021040108","volume":"15","author":"Y Labiod","year":"2021","unstructured":"Labiod Y, Korba AA, Ghoualmi-Zine N (2021) Detecting DDoS attacks in IoT environment. Int J Inf Secur Privacy (IJISP) 15(2):145\u201318","journal-title":"Int J Inf Secur Privacy (IJISP)"},{"issue":"14","key":"1650_CR2","doi-asserted-by":"crossref","first-page":"6274","DOI":"10.3390\/s23146274","volume":"23","author":"R Dass","year":"2023","unstructured":"Dass R, Narayanan M, Ananthakrishnan G, Murugan TK, Nallakaruppan MK, Somayaji SRK, Arputharaj K, Khan SB, Almusharraf A (2023) A cluster-based energy-efficient secure optimal path-routing protocol for wireless body-area sensor networks. Sensors 23(14):6274","journal-title":"Sensors"},{"issue":"3","key":"1650_CR3","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1007\/s11280-020-00855-2","volume":"24","author":"Y Al-Hadhrami","year":"2021","unstructured":"Al-Hadhrami Y, Hussain FK (2021) DDoS attacks in IoT networks: a comprehensive systematic literature review. World Wide Web 24(3):971\u20131001","journal-title":"World Wide Web"},{"key":"1650_CR4","doi-asserted-by":"crossref","unstructured":"Liang L, Zheng K, Sheng Q, Huang X (2016) A denial-of-service attack method for an IoT system. In: 2016 8th international conference on Information Technology in Medicine and Education (ITME). IEEE, pp 360\u2013364","DOI":"10.1109\/ITME.2016.0087"},{"key":"1650_CR5","first-page":"Article ID 3999","volume":"2022","author":"P Rana","year":"2022","unstructured":"Rana P, Batra I, Malik A, Imoize AL, Kim Y, Pani SK, Goyal N, Kumar A, Rho S (2022) Intrusion detection systems in cloud computing paradigm: analysis and overview. Complexity 2022:Article ID 3999039 (pp.1-14. IDS)","journal-title":"Complexity"},{"key":"1650_CR6","first-page":"Article ID 8981","volume":"2023","author":"SVN Santhosh Kumar","year":"2023","unstructured":"Santhosh Kumar SVN, Selvi M, Kannan A (2023) A comprehensive survey on machine learning-based intrusion detection systems for secure communication in internet of things. Comput Intell Neurosci 2023:Article ID 8981988","journal-title":"Comput Intell Neurosci"},{"issue":"1","key":"1650_CR7","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1504\/IJOR.2019.099545","volume":"35","author":"K Selvakumar","year":"2019","unstructured":"Selvakumar K, Sairamesh L, Kannan A (2019) Wise intrusion detection system using fuzzy rough set-based feature extraction and classification algorithms. Int J Oper Res 35(1):87\u2013107","journal-title":"Int J Oper Res"},{"issue":"5","key":"1650_CR8","doi-asserted-by":"crossref","first-page":"936","DOI":"10.3390\/agriculture13050936","volume":"13","author":"M Aggarwal","year":"2023","unstructured":"Aggarwal M, Khullar V, Goyal N, Singh A, Tolba A, Thompson EB, Kumar S (2023) Pre-trained deep neural network-based features selection supported machine learning for rice leaf disease classification. Agriculture 13(5):936","journal-title":"Agriculture"},{"key":"1650_CR9","unstructured":"Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department"},{"issue":"3","key":"1650_CR10","first-page":"1","volume":"9","author":"V Tereshko","year":"2005","unstructured":"Tereshko V, Loengarov A (2005) Collective decision making in honey-bee foraging dynamics. Comput Inf Syst 9(3):1","journal-title":"Comput Inf Syst"},{"issue":"4","key":"1650_CR11","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.undsp.2021.11.004","volume":"7","author":"T Feng","year":"2022","unstructured":"Feng T, Wang C, Zhang J, Wang B, Jin Y-F (2022) An improved artificial bee colony-random forest (IABC-RF) model for predicting the tunnel deformation due to an adjacent foundation pit excavation. Undergr Space 7(4):514\u2013527","journal-title":"Undergr Space"},{"key":"1650_CR12","doi-asserted-by":"crossref","unstructured":"Rangasamy RR, Duraisamy R (2019) Ensemble of artificial bee colony optimization and random forest technique for feature selection and classification of protein function family prediction. Soft computing in data analytics. Adv Intell Syst Comput 758","DOI":"10.1007\/978-981-13-0514-6_17"},{"key":"1650_CR13","doi-asserted-by":"crossref","unstructured":"Karaboga D, Basturk B (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. Advances in soft computing: foundations of fuzzy logic and soft computing, lecture notes in computer science, vol 4529\/2007, pp 789-798","DOI":"10.1007\/978-3-540-72950-1_77"},{"key":"1650_CR14","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.ins.2010.07.015","volume":"192","author":"B Akay","year":"2012","unstructured":"Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192:120\u2013142","journal-title":"Inf Sci"},{"key":"1650_CR15","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.amc.2009.03.090","volume":"214","author":"D Karaboga","year":"2009","unstructured":"Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108\u2013132","journal-title":"Appl Math Comput"},{"key":"1650_CR16","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.asoc.2014.06.035","volume":"23","author":"D Karaboga","year":"2014","unstructured":"Karaboga D, Gorkemli B (2014) A quick artificial bee colony (qABC) algorithm and its performance on optimization problems. Appl Soft Comput 23:227\u2013238","journal-title":"Appl Soft Comput"},{"issue":"4","key":"1650_CR17","first-page":"746","volume":"16","author":"B Senthilnayaki","year":"2019","unstructured":"Senthilnayaki B, Venkatalakshmi K, Kannan A (2019) Intrusion detection system using fuzzy rough set feature selection and modified KNN classifier. Int Arab J Inf Technol 16(4):746\u2013753","journal-title":"Int Arab J Inf Technol"},{"issue":"10","key":"1650_CR18","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.1109\/TAC.1997.633847","volume":"42","author":"JSR Jang","year":"1997","unstructured":"Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence. IEEE Trans Autom Control 42(10):1482\u20131484","journal-title":"IEEE Trans Autom Control"},{"issue":"3","key":"1650_CR19","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1007\/s11276-015-1013-1","volume":"22","author":"R Logambigai","year":"2016","unstructured":"Logambigai R, Kannan A (2016) Fuzzy logic based unequal clustering for wireless sensor networks. Wirel Netw 22(3):945\u2013957 (Springer)","journal-title":"Wirel Netw"},{"issue":"20","key":"1650_CR20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/s19204383","volume":"19","author":"M Alqahtani","year":"2019","unstructured":"Alqahtani M, Gumaei A, Mathkour H, Maher Ben Ismail M (2019) A genetic-based extreme gradient boosting model for detecting intrusions in wireless sensor networks. Sensors 19(20):1\u201320","journal-title":"Sensors"},{"issue":"11","key":"1650_CR21","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1145\/182.358434","volume":"26","author":"JF Allen","year":"1983","unstructured":"Allen JF (1983) Maintaining knowledge about temporal intervals. Commun ACM 26(11):832\u2013843","journal-title":"Commun ACM"},{"key":"1650_CR22","doi-asserted-by":"crossref","unstructured":"Heinzelman W, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocols for wireless microsensor networks. Proceedings of the 33rd Hawaaian International Conference on Systems Science (HICSS). pp 10","DOI":"10.1109\/HICSS.2000.926982"},{"issue":"17","key":"1650_CR23","doi-asserted-by":"crossref","DOI":"10.1002\/dac.5320","volume":"35","author":"M Nain","year":"2022","unstructured":"Nain M, Goyal N, Rani S, Popli R, Kansal I, Kaur P (2022) Hybrid optimization for fault-tolerant and accurate localization in mobility assisted underwater wireless sensor networks. Int J Commun Syst 35(17):e5320","journal-title":"Int J Commun Syst"},{"key":"1650_CR24","doi-asserted-by":"crossref","unstructured":"Subramani S, M S, A K, SVN SK (2023) Review of security methods based on classical cryptography and quantum cryptography. Cybern Syst:1\u201319","DOI":"10.1080\/01969722.2023.2166261"},{"key":"1650_CR25","doi-asserted-by":"crossref","first-page":"15201","DOI":"10.1007\/s00521-023-08511-2","volume":"35","author":"S Shalini","year":"2023","unstructured":"Shalini S, Selvi M (2023) Intelligent IDS in wireless sensor networks using deep fuzzy convolutional neural network. Neural Comput Appl 35:15201\u201315220 (Springer)","journal-title":"Neural Comput Appl"},{"key":"1650_CR26","doi-asserted-by":"publisher","first-page":"21954","DOI":"10.1109\/ACCESS.2017.2762418","volume":"5","author":"C Yin","year":"2017","unstructured":"Yin C, Zhu Y, Fei J, He X (2017) A deep learning approach for intrusion detection using recurrent neural networks. IEEEAccess 5:21954\u201321961. https:\/\/doi.org\/10.1109\/ACCESS.2017.2762418","journal-title":"IEEEAccess"},{"key":"1650_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-020-01623-2","author":"CM Hsu","year":"2020","unstructured":"Hsu CM, Azhari MZ, Hsieh HY, Prakosa SW, Leu JS (2020) Robust network intrusion detection scheme using long-short term memory based convolutional neural networks. Mobile Netw Appl. https:\/\/doi.org\/10.1007\/s11036-020-01623-2","journal-title":"Mobile Netw Appl"},{"issue":"4","key":"1650_CR28","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1007\/s10776-020-00495-3","volume":"27","author":"X Wang","year":"2020","unstructured":"Wang X, Yin S, Li H, Wang J, Teng L (2020) A network intrusion detection method based on deep multi-scale convolutional neural network. Int J Wirel Inf Networks 27(4):503\u2013517. https:\/\/doi.org\/10.1007\/s10776-020-00495-3","journal-title":"Int J Wirel Inf Networks"},{"key":"1650_CR29","doi-asserted-by":"crossref","unstructured":"Anand M, Kumar SP, Selvi M, SVN SK, Ram GD, Kannan A (2023) Deep learning model based ids for detecting cyber attacks in IoT based smart vehicle network. International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, pp 281\u2013286","DOI":"10.1109\/ICSCDS56580.2023.10104996"},{"issue":"11","key":"1650_CR30","first-page":"4506","volume":"12","author":"G Vanitha","year":"2021","unstructured":"Vanitha G (2021) Taylor based neuro-genetic algorithm for secure and energy aware multi hop routing protocol in WSN. Turk J Comput Math Educ (TURCOMAT) 12(11):4506\u20134515","journal-title":"Turk J Comput Math Educ (TURCOMAT)"},{"key":"1650_CR31","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s10207-020-00508-5","volume":"20","author":"SN Mighan","year":"2021","unstructured":"Mighan SN, Kahani M (2021) A novel scalable intrusion detection system based on deep learning. Int J Inf Secur 20:387\u2013403","journal-title":"Int J Inf Secur"},{"key":"1650_CR32","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s40537-021-00448-4","volume":"8","author":"F Laghrissi","year":"2021","unstructured":"Laghrissi F, Douzi S, Douzi K, Hssinarg B (2021) Intrusion detection systems using long short-term memory (LSTM). J Big Data 8:65","journal-title":"J Big Data"},{"key":"1650_CR33","volume":"10","author":"V Hnamte","year":"2023","unstructured":"Hnamte V, Hussain J (2023) DCNNBiLSTM: an efficient hybrid deep learning based intrusion detection system. Telematics Informatics Rep 10:100053","journal-title":"Telematics Informatics Rep"},{"key":"1650_CR34","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1007\/s42979-021-00991-0","volume":"3","author":"B Deore","year":"2022","unstructured":"Deore B, Bhosale S (2022) Intrusion detection system based on RNN classifier for feature reduction. SN Comput Sci 3:114","journal-title":"SN Comput Sci"},{"key":"1650_CR35","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.comcom.2022.12.010","volume":"199","author":"SM Kasongo","year":"2023","unstructured":"Kasongo SM (2023) A deep learning technique for intrusion detection system using a recurrent neural networks based framework. Comput Commun 199:113\u2013125","journal-title":"Comput Commun"},{"key":"1650_CR36","volume":"173","author":"B Li","year":"2021","unstructured":"Li B, Pi D, Lin Y, Cui L (2021) DNC: a deep neural network-based clustering-oriented network embedding algorithm. J Netw Comput Appl 173:102854","journal-title":"J Netw Comput Appl"},{"key":"1650_CR37","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1016\/j.psep.2023.03.052","volume":"173","author":"Z Guo","year":"2023","unstructured":"Guo Z, Yang C, Wang D, Liu H (2023) A novel deep learning model integrating CNN and GRU to predict particulate matter concentrations. Process Saf Environ Prot 173:604\u2013613","journal-title":"Process Saf Environ Prot"},{"key":"1650_CR38","doi-asserted-by":"crossref","unstructured":"Ajayi O, Cherian M, Saadawi T (2019) Secured cyber-attack signatures distribution using blockchain technology. In: 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). pp 482\u2013488","DOI":"10.1109\/CSE\/EUC.2019.00095"},{"key":"1650_CR39","doi-asserted-by":"crossref","unstructured":"Zheng S (2021) Network intrusion detection model based on convolutional neural network. In: 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), vol 5. pp 634\u2013637","DOI":"10.1109\/IAEAC50856.2021.9390930"},{"issue":"22","key":"1650_CR40","doi-asserted-by":"publisher","first-page":"17265","DOI":"10.1007\/s00500-020-05017-0","volume":"24","author":"B Riyaz","year":"2020","unstructured":"Riyaz B, Ganapathy S (2020) A deep learning approach for effective intrusion detection in wireless networks using CNN. Soft Comput 24(22):17265\u201317278. https:\/\/doi.org\/10.1007\/s00500-020-05017-0","journal-title":"Soft Comput"},{"key":"1650_CR41","doi-asserted-by":"crossref","DOI":"10.1016\/j.jnca.2021.103223","volume":"194","author":"M Krishnan","year":"2021","unstructured":"Krishnan M, Lim Y (2021) Reinforcement learning-based dynamic routing using mobile sink for data collection in WSNs and IoT applications. J Netw Comput Appl 194:103223","journal-title":"J Netw Comput Appl"},{"issue":"6","key":"1650_CR42","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1103\/PhysRev.28.1049","volume":"28","author":"E Schr\u00f6dinger","year":"1926","unstructured":"Schr\u00f6dinger E (1926) An undulatory theory of the mechanics of atoms and molecules. Phys Rev 28(6):1049","journal-title":"Phys Rev"},{"key":"1650_CR43","doi-asserted-by":"crossref","first-page":"170145","DOI":"10.1016\/j.ijleo.2022.170145","volume":"271","author":"K Hussain","year":"2022","unstructured":"Hussain K, Xia Y, Onaizah AN, Manzoor T, Jalil K (2022) Hybrid of WOA-ABC and proposed CNN for intrusion detection system in wireless sensor networks. Optik 271:170145","journal-title":"Optik"},{"issue":"18","key":"1650_CR44","doi-asserted-by":"crossref","first-page":"11484","DOI":"10.3390\/su141811484","volume":"14","author":"N Sharma","year":"2022","unstructured":"Sharma N, Gupta S, Mohamed HG, Anand D, Maz\u00f3n JLV, Gupta D, Goyal N (2022) Siamese convolutional neural network-based twin structure model for independent offline signature verification. Sustainability 14(18):11484","journal-title":"Sustainability"},{"key":"1650_CR45","doi-asserted-by":"crossref","unstructured":"Soe YN, Feng Y, Santosa PI, Hartanto R, Sakurai K (2019) Implementing lightweight IoT-IDS on raspberry pi using correlation-based feature selection and its performance evaluation. In: International Conference on advanced information networking and applications. Springer, pp 458\u2013469","DOI":"10.1007\/978-3-030-15032-7_39"},{"key":"1650_CR46","doi-asserted-by":"crossref","DOI":"10.1016\/j.scs.2020.102324","volume":"61","author":"MA Rahman","year":"2020","unstructured":"Rahman MA, Asyhari AT, Leong LS, Satrya GB, Tao MH, 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":"1650_CR47","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijcip.2021.100436","volume":"34","author":"F Medjek","year":"2021","unstructured":"Medjek F, Tandjaoui D, Djedjig N, Romdhani I (2021) Fault-tolerant AI-driven intrusion detection system for the internet of things. Int J Crit Infrastruct Prot 34:100436","journal-title":"Int J Crit Infrastruct Prot"},{"issue":"5","key":"1650_CR48","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1049\/iet-com.2019.0172","volume":"14","author":"P Nancy","year":"2020","unstructured":"Nancy P, Muthurajkumar S, Ganapathy S, Kumar SS, Selvi M, Arputharaj K (2020) Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks. IET Commun 14(5):888\u2013895","journal-title":"IET Commun"},{"issue":"4","key":"1650_CR49","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1007\/s11277-019-06155-x","volume":"105","author":"M Selvi","year":"2019","unstructured":"Selvi M, Thangaramya K, Ganapathy S, Kulothungan K, Khannah Nehemiah H, Kannan A (2019) An energy aware trust based secure routing algorithm for effective communication in wireless sensor networks. Wireless Pers Commun 105(4):1475\u20131490","journal-title":"Wireless Pers Commun"},{"key":"1650_CR50","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.comnet.2019.01.024","volume":"151","author":"K Thangaramya","year":"2019","unstructured":"Thangaramya K, Kulothungan K, Logambigai R, Selvi M, Ganapathy S, Kannan A (2019) Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Comput Netw 151:211\u2013223","journal-title":"Comput Netw"},{"issue":"14","key":"1650_CR51","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.1007\/s11082-023-05509-x","volume":"55","author":"S Godala","year":"2023","unstructured":"Godala S, Kumar MS (2023) A weight optimized deep learning model for cluster-based intrusion detection system. Opt Quant Electron 55(14):1224","journal-title":"Opt Quant Electron"},{"issue":"2","key":"1650_CR52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3578366","volume":"7","author":"N Sahani","year":"2023","unstructured":"Sahani N, Zhu R, Cho JH, Liu CC (2023) Machine learning-based intrusion detection for smart grid computing: a survey. ACM Trans Cyber-Phys Syst 7(2):1\u201331","journal-title":"ACM Trans Cyber-Phys Syst"},{"key":"1650_CR53","doi-asserted-by":"crossref","unstructured":"Nayak S, Ahmed N, Misra S (2021) Deep learning-based reliableroutingattackdetectionmechanismforindustrialinternetofthings. Elsevier, AdHocNetworks123 | 102661, vol 123, pp 1\u201311","DOI":"10.1016\/j.adhoc.2021.102661"},{"key":"1650_CR54","doi-asserted-by":"crossref","first-page":"4778","DOI":"10.1007\/s11227-020-03471-z","volume":"77","author":"S Sharma","year":"2020","unstructured":"Sharma S, Verma VK (2020) Security explorations for routing attacks inlowpowernetworksonInternetofThings. J Super Comput 77:4778\u20134812 (SpringerLink)","journal-title":"J Super Comput"},{"key":"1650_CR55","doi-asserted-by":"crossref","first-page":"13757","DOI":"10.1007\/s11227-021-03833-1","volume":"77","author":"S Sharma","year":"2021","unstructured":"Sharma S, Verma VK (2021) AIEMLA: artificial intelligence enabled machine learning approach for routing attacks on internet of things. J Super Comput 77:13757\u201313787 (SpringerLink)","journal-title":"J Super Comput"},{"issue":"6","key":"1650_CR56","first-page":"1","volume":"8","author":"SM Muzammal","year":"2020","unstructured":"Muzammal SM, Murugesan RK, Jhanjhi NZ (2020) A comprehensivereview on secure routing in internet of things: mitigation methods and trust-based approaches. IEEE J Mag 8(6):1\u201325 (IEEE Xplore)","journal-title":"IEEE J Mag"},{"issue":"8","key":"1650_CR57","first-page":"1","volume":"8","author":"MM Hassan","year":"2020","unstructured":"Hassan MM, Hassan MDR, Huda S, deAlbuquerque VHC (2020) A robust deep-learning enabled trust boundary protection for adversarial industrial IoT environment. IEEE J Mag 8(8):1\u201312","journal-title":"IEEE J Mag"},{"key":"1650_CR58","doi-asserted-by":"crossref","unstructured":"Ning B, Qiu S, Zhao T, Li Y (2020) Power IoT attack samples generation and detection using generative adversarial networks. IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), pp 3721\u20133724","DOI":"10.1109\/EI250167.2020.9346661"},{"key":"1650_CR59","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1109\/TSE.1987.232894","volume":"2","author":"E Denning Dorothy","year":"1987","unstructured":"Denning Dorothy E (1987) An intrusion-detection model. IEEE Trans Software Eng 2:222\u2013232","journal-title":"IEEE Trans Software Eng"},{"issue":"1","key":"1650_CR60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2808691","volume":"48","author":"A Milenkoski","year":"2015","unstructured":"Milenkoski A, Vieira M, Kounev S, Avritzer A, Payne BD (2015) Evaluating computer intrusion detection systems: a survey of common practices. ACM Comput Surv (CSUR) 48(1):1\u201341","journal-title":"ACM Comput Surv (CSUR)"},{"key":"1650_CR61","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.jnca.2017.03.018","volume":"87","author":"W Haider","year":"2017","unstructured":"Haider W, Hu J, Slay J, Turnbull BP, Xie Y (2017) Generating realistic intrusion detection system dataset based on fuzzy qualitative modeling. J Netw Comput Appl 87:185\u2013192","journal-title":"J Netw Comput Appl"},{"key":"1650_CR62","first-page":"1","volume":"226","author":"PR Kanna","year":"2021","unstructured":"Kanna PR, Santhi P (2021) Unified deep learning approach for efficient intrusion detection system using integrated spatial\u2013temporal features. Knowl-Based Syst 226:1\u201312","journal-title":"Knowl-Based Syst"},{"key":"1650_CR63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2020.100772","volume":"60","author":"BM Sahoo","year":"2021","unstructured":"Sahoo BM, Pandey HM, Amgoth T (2021) GAPSO-H: a hybrid approach towards optimizing the cluster-based routing in wireless sensor network. Swarm Evol Comput 60:1\u201342","journal-title":"Swarm Evol Comput"},{"issue":"2","key":"1650_CR64","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s11277-020-07882-2","volume":"117","author":"S Prithi","year":"2021","unstructured":"Prithi S, Sumathi S (2021) Automata based hybrid PSO\u2013GWO algorithm for secured energy efficient optimal routing in wireless sensor network. Wireless Pers Commun 117(2):545\u2013559","journal-title":"Wireless Pers Commun"},{"issue":"1","key":"1650_CR65","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s11277-020-07376-1","volume":"114","author":"M Boulaiche","year":"2020","unstructured":"Boulaiche M (2020) Survey of secure routing protocols for wireless ad hoc networks. Wireless Pers Commun 114(1):483\u2013517","journal-title":"Wireless Pers Commun"},{"issue":"7","key":"1650_CR66","first-page":"763","volume":"32","author":"O Deepa","year":"2020","unstructured":"Deepa O, Suguna J (2020) An optimized QoS-based clustering with multipath routing protocol for wireless sensor networks. J King Saud Univ-Comput Inf Sci 32(7):763\u2013774","journal-title":"J King Saud Univ-Comput Inf Sci"},{"key":"1650_CR67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.comnet.2019.107042","volume":"168","author":"W Elmasry","year":"2020","unstructured":"Elmasry W, Akbulut A, Zaim AH (2020) Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic. Comput Netw 168:1\u20132","journal-title":"Comput Netw"},{"issue":"3\u20134","key":"1650_CR68","first-page":"439","volume":"20","author":"S Shalini","year":"2023","unstructured":"Shalini S, Selvi M (2023) Intrusion detection system using RBPSO and fuzzy neuro-genetic classification algorithms in wireless sensor networks. Int J Inf Comput Secur 20(3\u20134):439\u2013461 (Inderscience)","journal-title":"Int J Inf Comput Secur"},{"issue":"3\u20134","key":"1650_CR69","first-page":"414","volume":"20","author":"S Shalini","year":"2023","unstructured":"Shalini S, Selvi M (2023) Comprehensive review on distributed denial of service attacks in wireless sensor networks. Int J Inf Comput Secur 20(3\u20134):414\u2013438 (Inderscience)","journal-title":"Int J Inf Comput Secur"},{"key":"1650_CR70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2019.100631","volume":"53","author":"A Thakkar","year":"2020","unstructured":"Thakkar A, Lohiya R (2020) Role of swarm and evolutionary algorithms for intrusion detection system: a survey. Swarm Evol Comput 53:1\u201334","journal-title":"Swarm Evol Comput"},{"key":"1650_CR71","doi-asserted-by":"crossref","unstructured":"Moraboena S, Ketepalli G, Ragam P (2020) A deep learning approach to network intrusion detection using deep autoencoder. Rev Intell Artif 34(4)","DOI":"10.18280\/ria.340410"},{"issue":"June","key":"1650_CR72","doi-asserted-by":"publisher","first-page":"107247","DOI":"10.1016\/j.comnet.2020.107247","volume":"174","author":"Y Zhou","year":"2020","unstructured":"Zhou Y, Cheng G, Jiang S, Dai M (2020) Building an efficient intrusion detection system based on feature selection and ensemble classifier. Comput Netw 174(June):107247. https:\/\/doi.org\/10.1016\/j.comnet.2020.107247","journal-title":"Comput Netw"},{"key":"1650_CR73","doi-asserted-by":"crossref","first-page":"138451","DOI":"10.1109\/ACCESS.2021.3116219","volume":"9","author":"S Seth","year":"2021","unstructured":"Seth S, Chahal KK, Singh G (2021) A novel ensemble framework for an intelligent intrusion detection system. IEEE Access 9:138451\u2013138467","journal-title":"IEEE Access"},{"key":"1650_CR74","doi-asserted-by":"crossref","DOI":"10.1016\/j.iot.2021.100435","volume":"16","author":"A Alhowaide","year":"2021","unstructured":"Alhowaide A, Alsmadi I, Tang J (2021) Ensemble detection model for IoT IDS. Internet Things 16:100435","journal-title":"Internet Things"},{"key":"1650_CR75","doi-asserted-by":"crossref","first-page":"94497","DOI":"10.1109\/ACCESS.2019.2928048","volume":"7","author":"BA Tama","year":"2019","unstructured":"Tama BA, Comuzzi M, Rhee KH (2019) TSE-IDS: a two-stage classifier ensemble for intelligent anomaly-based intrusion detection system. IEEE Access 7:94497\u201394507","journal-title":"IEEE Access"},{"key":"1650_CR76","doi-asserted-by":"crossref","first-page":"102499","DOI":"10.1016\/j.cose.2021.102499","volume":"112","author":"N Gupta","year":"2022","unstructured":"Gupta N, Jindal V, Bedi P (2022) CSE-IDS: using cost-sensitive deep learning and ensemble algorithms to handle class imbalance in network-based intrusion detection systems. Comput Secur 112:102499","journal-title":"Comput Secur"},{"key":"1650_CR77","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.cose.2016.11.004","volume":"65","author":"AA Aburomman","year":"2017","unstructured":"Aburomman AA, Reaz MBI (2017) A survey of intrusion detection systems based on ensemble and hybrid classifiers. Comput Secur 65:135\u2013152","journal-title":"Comput Secur"},{"key":"1650_CR78","doi-asserted-by":"crossref","first-page":"6930","DOI":"10.1109\/ACCESS.2020.3046246","volume":"9","author":"D Stiawan","year":"2020","unstructured":"Stiawan D, Heryanto A, Bardadi A, Rini DP, Subroto IMI, Idris MYB, Budiarto R (2020) An approach for optimizing ensemble intrusion detection systems. IEEE Access 9:6930\u20136947","journal-title":"IEEE Access"},{"issue":"1","key":"1650_CR79","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1109\/TCYB.2022.3163811","volume":"53","author":"L Vu","year":"2022","unstructured":"Vu L, Nguyen QU, Nguyen DN, Hoang DT, Dutkiewicz E (2022) Deep generative learning models for cloud intrusion detection systems. IEEE Trans Cybern 53(1):565\u2013577","journal-title":"IEEE Trans Cybern"},{"key":"1650_CR80","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.neunet.2021.10.021","volume":"145","author":"A Ali","year":"2022","unstructured":"Ali A, Zhu Y, Zakarya M (2022) Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction. Neural Netw 145:233\u2013247","journal-title":"Neural Netw"},{"key":"1650_CR81","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2018","unstructured":"Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, Chen T (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354\u2013377","journal-title":"Pattern Recogn"},{"key":"1650_CR82","doi-asserted-by":"crossref","first-page":"9438","DOI":"10.1007\/s11227-021-04285-3","volume":"78","author":"AS Alqahtani","year":"2022","unstructured":"Alqahtani AS (2022) FSO-LSTM IDS: hybrid optimized and ensembled deep-learning network-based intrusion detection system for smart networks. J Super Comput 78:9438\u20139455","journal-title":"J Super Comput"}],"container-title":["Peer-to-Peer Networking and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12083-024-01650-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12083-024-01650-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12083-024-01650-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,9]],"date-time":"2024-06-09T13:05:18Z","timestamp":1717938318000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12083-024-01650-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,16]]},"references-count":82,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["1650"],"URL":"https:\/\/doi.org\/10.1007\/s12083-024-01650-w","relation":{},"ISSN":["1936-6442","1936-6450"],"issn-type":[{"value":"1936-6442","type":"print"},{"value":"1936-6450","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,16]]},"assertion":[{"value":"22 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not Applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal ethics"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}