{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T21:21:44Z","timestamp":1772313704806,"version":"3.50.1"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T00:00:00Z","timestamp":1625616000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T00:00:00Z","timestamp":1625616000000},"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":["Appl Intell"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s10489-021-02621-x","type":"journal-article","created":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T09:19:55Z","timestamp":1625649595000},"page":"3527-3544","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["A lightweight intelligent network intrusion detection system using OCSVM and Pigeon inspired optimizer"],"prefix":"10.1007","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6768-9696","authenticated-orcid":false,"given":"Hadeel","family":"Alazzam","sequence":"first","affiliation":[]},{"given":"Ahmad","family":"Sharieh","sequence":"additional","affiliation":[]},{"given":"Khair Eddin","family":"Sabri","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"key":"2621_CR1","first-page":"28","volume":"128","author":"A Abdiansah","year":"2015","unstructured":"Abdiansah A, Wardoyo R (2015) Time complexity analysis of support vector machines (svm) in libsvm. Int J Comput Appl 128:28\u201334","journal-title":"Int J Comput Appl"},{"key":"2621_CR2","doi-asserted-by":"crossref","unstructured":"Aggarwal A, Sahay T, Bansal A, Chandra M (2015) Grid search analysis of nu-svc for text-dependent speaker-identification. In: 2015 Annual IEEE india conference (INDICON). IEEE, pp 1\u20135","DOI":"10.1109\/INDICON.2015.7443790"},{"key":"2621_CR3","doi-asserted-by":"crossref","unstructured":"Al-Azzam S, Sharieh A, Al-Sharaeh S, Azzam N (2020) A data estimation for failing nodes using fuzzy logic with integrated microcontroller in wireless sensor networks. Int J Electric Comput Eng (2088-8708) 10","DOI":"10.11591\/ijece.v10i4.pp3623-3634"},{"key":"2621_CR4","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.eswa.2016.09.041","volume":"67","author":"WL Al-Yaseen","year":"2017","unstructured":"Al-Yaseen WL, Othman ZA, Nazri MZA (2017) Multi-level hybrid support vector machine and extreme learning machine based on modified k-means for intrusion detection system. Expert Syst Appl 67:296\u2013303","journal-title":"Expert Syst Appl"},{"key":"2621_CR5","doi-asserted-by":"crossref","unstructured":"Alazzam H, Alsmady A, Shorman AA (2019) Supervised detection of iot botnet attacks. In: Proceedings of the second international conference on data science, E-Learning and information systems, pp 1\u20136","DOI":"10.1145\/3368691.3368733"},{"key":"2621_CR6","doi-asserted-by":"publisher","first-page":"113249","DOI":"10.1016\/j.eswa.2020.113249","volume":"148","author":"H Alazzam","year":"2020","unstructured":"Alazzam H, Sharieh A, Sabri KE (2020) A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer. Expert Syst Appl 148:113249","journal-title":"Expert Syst Appl"},{"key":"2621_CR7","first-page":"83","volume":"12","author":"L Albdour","year":"2020","unstructured":"Albdour L, Manaseer S, Sharieh A (2020) Iot crawler with behavior analyzer at fog layer for detecting malicious nodes. Int J Commun Netw Inform Secur 12:83\u201394","journal-title":"Int J Commun Netw Inform Secur"},{"key":"2621_CR8","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.comcom.2016.12.007","volume":"98","author":"AA Amaral","year":"2017","unstructured":"Amaral AA, de Souza Mendes L, Zarpel\u00e3o BB, Junior MLP (2017) Deep ip flow inspection to detect beyond network anomalies. Comput Commun 98:80\u201396","journal-title":"Comput Commun"},{"key":"2621_CR9","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.1007\/s00521-015-1964-2","volume":"27","author":"B Aslahi-Shahri","year":"2016","unstructured":"Aslahi-Shahri B, Rahmani R, Chizari M, Maralani A, Eslami M, Golkar MJ, Ebrahimi A (2016) A hybrid method consisting of ga and svm for intrusion detection system. Neural comput Appl 27:1669\u20131676","journal-title":"Neural comput Appl"},{"key":"2621_CR10","first-page":"26","volume":"1","author":"M Bahrololum","year":"2009","unstructured":"Bahrololum M, Salahi E, Khaleghi M (2009) Anomaly intrusion detection design using hybrid of unsupervised and supervised neural network. Int J Comput Netw Commun (IJCNC) 1:26\u201333","journal-title":"Int J Comput Netw Commun (IJCNC)"},{"key":"2621_CR11","doi-asserted-by":"publisher","first-page":"2315","DOI":"10.1007\/s10489-017-1085-y","volume":"48","author":"I Benmessahel","year":"2018","unstructured":"Benmessahel I, Xie K, Chellal M (2018) A new evolutionary neural networks based on intrusion detection systems using multiverse optimization. Appl Intell 48:2315\u20132327","journal-title":"Appl Intell"},{"key":"2621_CR12","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s12065-019-00199-5","volume":"12","author":"I Benmessahel","year":"2019","unstructured":"Benmessahel I, Xie K, Chellal M, Semong T (2019) A new evolutionary neural networks based on intrusion detection systems using locust swarm optimization. Evol Intel 12:131\u2013146","journal-title":"Evol Intel"},{"key":"2621_CR13","doi-asserted-by":"publisher","first-page":"102225","DOI":"10.1016\/j.cose.2021.102225","volume":"104","author":"EK Boahen","year":"2021","unstructured":"Boahen EK, Bouya-Moko BE, Wang C (2021) Network anomaly detection in a controlled environment based on an enhanced psogsarfc. Comput Secur 104:102225","journal-title":"Comput Secur"},{"key":"2621_CR14","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1016\/j.cose.2011.08.006","volume":"30","author":"C Callegari","year":"2011","unstructured":"Callegari C, Giordano S, Pagano M, Pepe T (2011) Combining sketches and wavelet analysis for multi time-scale network anomaly detection. Comput Secur 30:692\u2013704","journal-title":"Comput Secur"},{"key":"2621_CR15","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.procs.2015.04.007","volume":"50","author":"J David","year":"2015","unstructured":"David J, Thomas C (2015) Ddos attack detection using fast entropy approach on flow-based network traffic. Procedia Comput Sci 50:30\u201336","journal-title":"Procedia Comput Sci"},{"key":"2621_CR16","first-page":"70","volume":"12","author":"F Farahnakian","year":"2018","unstructured":"Farahnakian F (2018) Anomaly-based intrusion detection using deep neural networks. Int J Digit Content Technol Appl 12:70\u201318","journal-title":"Int J Digit Content Technol Appl"},{"key":"2621_CR17","doi-asserted-by":"crossref","unstructured":"Farahnakian F, Heikkonen J (2018) A deep auto-encoder based approach for intrusion detection system. In: 2018 20th international conference on advanced communication technology (ICACT). IEEE, pp 178\u2013183","DOI":"10.23919\/ICACT.2018.8323687"},{"key":"2621_CR18","unstructured":"Fourie C, Van Niekerk A, Mucina L (2011) Optimising a one-class svm for geographic object-based novelty detection. In: Proceedings of the first AfricaGeo conference, pp 1\u201325"},{"key":"2621_CR19","first-page":"3","volume":"9","author":"W Gao","year":"2014","unstructured":"Gao W, Morris TH (2014) On cyber attacks and signature based intrusion detection for modbus based industrial control systems. J Digit Forens Secur Law 9:3","journal-title":"J Digit Forens Secur Law"},{"key":"2621_CR20","doi-asserted-by":"crossref","unstructured":"Ghafoori Z, Rajasegarar S, Erfani SM, Karunasekera S, Leckie CA (2016) Unsupervised parameter estimation for one-class support vector machines. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp 183\u2013195","DOI":"10.1007\/978-3-319-31750-2_15"},{"key":"2621_CR21","doi-asserted-by":"crossref","unstructured":"Ghanem WAH, Jantan A, Ghaleb SAA, Nasser AB (2020) An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons, vol 8, pp 130452\u2013130475","DOI":"10.1109\/ACCESS.2020.3009533"},{"key":"2621_CR22","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.inffus.2006.10.002","volume":"9","author":"G Giacinto","year":"2008","unstructured":"Giacinto G, Perdisci R, Del Rio M, Roli F (2008) Intrusion detection in computer networks by a modular ensemble of one-class classifiers. Inform Fusion 9:69\u201382","journal-title":"Inform Fusion"},{"key":"2621_CR23","doi-asserted-by":"publisher","first-page":"102158","DOI":"10.1016\/j.cose.2020.102158","volume":"103","author":"J Gu","year":"2021","unstructured":"Gu J, Lu S (2021) An effective intrusion detection approach using svm with na\u00efve bayes feature embedding. Comput Secur 103:102158","journal-title":"Comput Secur"},{"key":"2621_CR24","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1016\/j.eswa.2017.09.013","volume":"92","author":"AH Hamamoto","year":"2018","unstructured":"Hamamoto AH, Carvalho LF, Sampaio LDH, Abr\u00e3o T., Proen\u00e7a ML Jr (2018) Network anomaly detection system using genetic algorithm and fuzzy logic. Expert Syst Appl 92:390\u2013402","journal-title":"Expert Syst Appl"},{"key":"2621_CR25","doi-asserted-by":"publisher","first-page":"3203","DOI":"10.1016\/j.comcom.2007.05.061","volume":"30","author":"M Hamdi","year":"2007","unstructured":"Hamdi M, Boudriga N (2007) Detecting denial-of-service attacks using the wavelet transform. Comput Commun 30:3203\u20133213","journal-title":"Comput Commun"},{"key":"2621_CR26","doi-asserted-by":"publisher","first-page":"65","DOI":"10.4018\/IJCRE.2021010107","volume":"3","author":"S Helser","year":"2021","unstructured":"Helser S, Hwang MI (2021) Identity theft: a review of critical issues. Int J Cyber Res Educ (IJCRE) 3:65\u201377","journal-title":"Int J Cyber Res Educ (IJCRE)"},{"key":"2621_CR27","doi-asserted-by":"crossref","unstructured":"Holm H (2014) Signature based intrusion detection for zero-day attacks:(not) a closed chapter?. In: 2014 47th Hawaii international conference on system sciences. IEEE, pp 4895\u20134904","DOI":"10.1109\/HICSS.2014.600"},{"key":"2621_CR28","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.eswa.2010.06.066","volume":"38","author":"S-J Horng","year":"2011","unstructured":"Horng S-J, Su M-Y, Chen Y-H, Kao T-W, Chen R-J, Lai J-L, Perkasa CD (2011) A novel intrusion detection system based on hierarchical clustering and support vector machines. Expert Syst Appl 38:306\u2013313","journal-title":"Expert Syst Appl"},{"key":"2621_CR29","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.eswa.2018.04.038","volume":"108","author":"A Karami","year":"2018","unstructured":"Karami A (2018) An anomaly-based intrusion detection system in presence of benign outliers with visualization capabilities. Expert Syst Appl 108:36\u201360","journal-title":"Expert Syst Appl"},{"key":"2621_CR30","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.jnca.2012.09.004","volume":"36","author":"H-J Liao","year":"2013","unstructured":"Liao H-J, Lin C-HR, Lin Y-C, Tung K-Y (2013) Intrusion detection system: A comprehensive review. J Netw Comput Appl 36:16\u201324","journal-title":"J Netw Comput Appl"},{"key":"2621_CR31","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/S0031-3203(02)00060-2","volume":"36","author":"A Likas","year":"2003","unstructured":"Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recognit 36:451\u2013461","journal-title":"Pattern Recognit"},{"key":"2621_CR32","doi-asserted-by":"publisher","first-page":"3285","DOI":"10.1016\/j.asoc.2012.05.004","volume":"12","author":"S-W Lin","year":"2012","unstructured":"Lin S-W, Ying K-C, Lee C-Y, Lee Z-J (2012) An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection. Appl Soft Comput 12:3285\u20133290","journal-title":"Appl Soft Comput"},{"key":"2621_CR33","doi-asserted-by":"crossref","unstructured":"Mantovani RG, Rossi AL, Vanschoren J, Bischl B, De Carvalho AC (2015) Effectiveness of random search in svm hyper-parameter tuning. In: 2015 international joint conference on neural networks (IJCNN). Ieee, pp 1\u20138","DOI":"10.1109\/IJCNN.2015.7280664"},{"key":"2621_CR34","first-page":"4166","volume":"4107","author":"MA Manzoor","year":"2017","unstructured":"Manzoor MA, Morgan Y (2017) Network intrusion detection system using apache storm. Probe 4107:4166","journal-title":"Probe"},{"key":"2621_CR35","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/MPRV.2018.03367731","volume":"17","author":"Y Meidan","year":"2018","unstructured":"Meidan Y, Bohadana M, Mathov Y, Mirsky Y, Shabtai A, Breitenbacher D, Elovici Y (2018) N-baiot\u2014network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Comput 17:12\u201322","journal-title":"IEEE Pervasive Comput"},{"key":"2621_CR36","doi-asserted-by":"publisher","first-page":"3883","DOI":"10.1002\/sec.1307","volume":"8","author":"W Meng","year":"2015","unstructured":"Meng W, Li W, Kwok L-F (2015) Design of intelligent knn-based alarm filter using knowledge-based alert verification in intrusion detection. Secur Commun Netw 8:3883\u20133895","journal-title":"Secur Commun Netw"},{"key":"2621_CR37","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":"2621_CR38","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: 2015 military communications and information systems conference (MilCIS). IEEE, pp 1\u20136","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"2621_CR39","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 algorithm. Procedia Eng 30:174\u2013182","journal-title":"Procedia Eng"},{"key":"2621_CR40","first-page":"92","volume":"3","author":"O Niaksu","year":"2015","unstructured":"Niaksu O (2015) Crisp data mining methodology extension for medical domain. Baltic J Modern Comput 3:92","journal-title":"Baltic J Modern Comput"},{"key":"2621_CR41","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1109\/TSP.2015.2504345","volume":"64","author":"H Ozkan","year":"2015","unstructured":"Ozkan H, Ozkan F, Kozat SS (2015) Online anomaly detection under markov statistics with controllable type-i error. IEEE Trans Signal Process 64:1435\u20131445","journal-title":"IEEE Trans Signal Process"},{"key":"2621_CR42","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.jnca.2005.06.003","volume":"30","author":"S Peddabachigari","year":"2007","unstructured":"Peddabachigari S, Abraham A, Grosan C, Thomas J (2007) Modeling intrusion detection system using hybrid intelligent systems. J Netw Comput Appl 30:114\u2013132","journal-title":"J Netw Comput Appl"},{"key":"2621_CR43","doi-asserted-by":"crossref","unstructured":"P\u00e9rez D, Alonso S, Mor\u00e1n A, Prada MA, Fuertes JJ, Dom\u00ednguez M (2019) Comparison of network intrusion detection performance using feature representation. In: International conference on engineering applications of neural networks. Springer, pp 463\u2013475","DOI":"10.1007\/978-3-030-20257-6_40"},{"key":"2621_CR44","doi-asserted-by":"crossref","unstructured":"Prasad R, Rohokale V (2020) Artificial intelligence and machine learning in cyber security. In: Cyber Security: The lifeline of information and communication technology. Springer, pp 231\u2013247","DOI":"10.1007\/978-3-030-31703-4_16"},{"key":"2621_CR45","first-page":"8887","volume":"975","author":"M Qatawneh","year":"2020","unstructured":"Qatawneh M, Almobaideen W, AbuAlghanam O (2020) Challenges of blockchain technology in context internet of things: A survey. Int J Comput Appl 975:8887","journal-title":"Int J Comput Appl"},{"key":"2621_CR46","doi-asserted-by":"publisher","unstructured":"Faris H, Castillo P, Merelo Guerv\u00f3s J, Al-Madi N (2018) The influence of input data standardization methods on the prediction accuracy of genetic programming generated classifiers. In: The 10th international joint conference on computational intelligence. https:\/\/doi.org\/10.5220\/0006959000790085, pp 79\u201385","DOI":"10.5220\/0006959000790085"},{"key":"2621_CR47","first-page":"3","volume":"23","author":"E Rahm","year":"2000","unstructured":"Rahm E, Do HH (2000) Data cleaning: Problems and current approaches. IEEE Data Eng Bull 23:3\u201313","journal-title":"IEEE Data Eng Bull"},{"key":"2621_CR48","doi-asserted-by":"publisher","first-page":"44","DOI":"10.17485\/ijst\/2014\/v7sp5.20","volume":"7","author":"SB Rajakumari","year":"2014","unstructured":"Rajakumari SB, Nalini C (2014) An efficient data mining dataset preparation using aggregation in relational database. Indian J Sci Technol 7:44","journal-title":"Indian J Sci Technol"},{"key":"2621_CR49","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1016\/j.procs.2015.03.174","volume":"45","author":"U Ravale","year":"2015","unstructured":"Ravale U, Marathe N, Padiya P (2015) Feature selection based hybrid anomaly intrusion detection system using k means and rbf kernel function. Procedia Comput Sci 45:428\u2013435","journal-title":"Procedia Comput Sci"},{"key":"2621_CR50","doi-asserted-by":"crossref","unstructured":"Ren J, Guo J, Qian W, Yuan H, Hao X, Jingjing H (2019) Building an effective intrusion detection system by using hybrid data optimization based on machine learning algorithms. Secur Commun Netw","DOI":"10.1155\/2019\/7130868"},{"key":"2621_CR51","first-page":"1848","volume":"2","author":"S Revathi","year":"2013","unstructured":"Revathi S, Malathi A (2013) A detailed analysis on nsl-kdd dataset using various machine learning techniques for intrusion detection. Int J Eng Res Technol (IJERT) 2:1848\u20131853","journal-title":"Int J Eng Res Technol (IJERT)"},{"key":"2621_CR52","unstructured":"Roesch M et al (1999) Snort: Lightweight intrusion detection for networks. In: Lisa, vol 99, pp 229\u2013238"},{"key":"2621_CR53","unstructured":"Sanjaya SKSSS, Jena K (2014) A detail analysis on intrusion detection datasets. In: 2014 IEEE International Advance Computing Conference (IACC)"},{"key":"2621_CR54","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/S0167-9473(03)00177-4","volume":"45","author":"SL Scott","year":"2004","unstructured":"Scott SL (2004) A bayesian paradigm for designing intrusion detection systems. Comput Stat Data Anal 45:69\u201383","journal-title":"Comput Stat Data Anal"},{"key":"2621_CR55","unstructured":"Shewale VR, Patil HD (2016) Performance evaluation of attack detection algorithms using improved hybrid ids with online captured data. Int J Comput Appl"},{"key":"2621_CR56","doi-asserted-by":"crossref","unstructured":"Siddique K, Akhtar Z, Khan MA, Jung Y-H, Kim Y (2018) Developing an intrusion detection framework for high-speed big data networks: a comprehensive approach. KSII Trans Int Inform Syst 12","DOI":"10.3837\/tiis.2018.08.026"},{"key":"2621_CR57","doi-asserted-by":"crossref","unstructured":"Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the kdd cup 99 data set. In: 2009 IEEE symposium on computational intelligence for security and defense applications. IEEE, pp 1\u20136","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"2621_CR58","unstructured":"Thakral A, Rakesh N, Gupta A (2012) Area prone to cyber attacks. CSI Communications"},{"key":"2621_CR59","doi-asserted-by":"crossref","unstructured":"Truong TC, Zelinka I, Plucar J, \u010cand\u00edk M, \u0160ulc V (2020) Artificial intelligence and cybersecurity: Past, presence, and future. In: Artificial intelligence and evolutionary computations in engineering systems. Springer, pp 351\u2013363","DOI":"10.1007\/978-981-15-0199-9_30"},{"key":"2621_CR60","first-page":"13","volume":"10","author":"L Van Der Maaten","year":"2009","unstructured":"Van Der Maaten L, Postma E, Van den Herik J (2009) Dimensionality reduction: a comparative. J Mach Learn Res 10:13","journal-title":"J Mach Learn Res"},{"key":"2621_CR61","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.cose.2013.04.004","volume":"38","author":"R Von Solms","year":"2013","unstructured":"Von Solms R, Van Niekerk J (2013) From information security to cyber security. Comput Secur 38:97\u2013102","journal-title":"Comput Secur"},{"key":"2621_CR62","doi-asserted-by":"publisher","first-page":"6225","DOI":"10.1016\/j.eswa.2010.02.102","volume":"37","author":"G Wang","year":"2010","unstructured":"Wang G, Hao J, Ma J, Huang L (2010) A new approach to intrusion detection using artificial neural networks and fuzzy clustering. Exp Syst Appl 37:6225\u20136232","journal-title":"Exp Syst Appl"},{"key":"2621_CR63","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1016\/j.compmedimag.2012.07.004","volume":"36","author":"W-J Wu","year":"2012","unstructured":"Wu W-J, Lin S-W, Moon WK (2012) Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images. Comput Med Imaging Graph 36:627\u2013 633","journal-title":"Comput Med Imaging Graph"},{"key":"2621_CR64","doi-asserted-by":"publisher","first-page":"626","DOI":"10.3390\/s21020626","volume":"21","author":"R Yao","year":"2021","unstructured":"Yao R, Wang N, Liu Z, Chen P, Sheng X (2021) Intrusion detection system in the advanced metering infrastructure: a cross-layer feature-fusion cnn-lstm-based approach. Sensors 21:626","journal-title":"Sensors"},{"key":"2621_CR65","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1023\/A:1024600519144","volume":"9","author":"Y Zhang","year":"2003","unstructured":"Zhang Y, Lee W, Huang Y-A (2003) Intrusion detection techniques for mobile wireless networks. Wirel Netw 9:545\u2013 556","journal-title":"Wirel Netw"},{"key":"2621_CR66","unstructured":"Zong B, Song Q, Min MR, Cheng W, Lumezanu C, Cho D, Chen H (2018) Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: ICLR"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02621-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02621-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02621-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,4]],"date-time":"2023-02-04T23:10:38Z","timestamp":1675552238000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02621-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,7]]},"references-count":66,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["2621"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02621-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,7]]},"assertion":[{"value":"15 June 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}