{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T16:43:42Z","timestamp":1751388222906,"version":"3.37.3"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s00521-022-07060-4","type":"journal-article","created":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T09:02:32Z","timestamp":1646125352000},"page":"15207-15220","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Variational restricted Boltzmann machines to automated anomaly detection"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1330-5228","authenticated-orcid":false,"given":"Konstantinos","family":"Demertzis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lazaros","family":"Iliadis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elias","family":"Pimenidis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panagiotis","family":"Kikiras","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,1]]},"reference":[{"key":"7060_CR1","doi-asserted-by":"publisher","unstructured":"Boubekeur M (2017) Industrial applications for cyber-physical systems. In: 2017 first international conference on embedded distributed systems (EDiS), pp 59\u201359. https:\/\/doi.org\/10.1109\/EDIS.2017.8284020","DOI":"10.1109\/EDIS.2017.8284020"},{"key":"7060_CR2","unstructured":"Banafa A (2018) 2 The Industrial Internet of Things (IIoT): challenges, requirements and benefits. In: Secure and smart internet of things (IoT): using blockchain and AI, river publishers, pp 7\u201312. [Online]. https:\/\/ieeexplore.ieee.org\/document\/9226906. Accessed 19 Jan 2021"},{"key":"7060_CR3","doi-asserted-by":"crossref","unstructured":"Radanliev P et al (2020) Cyber risk at the edge: current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains. [Online]. https:\/\/www.preprints.org\/manuscript\/201903.0123\/v2. Accessed 19 Jan 2021","DOI":"10.1186\/s42400-020-00052-8"},{"issue":"23","key":"7060_CR4","doi-asserted-by":"publisher","first-page":"17361","DOI":"10.1007\/s00521-020-05189-8","volume":"32","author":"K Demertzis","year":"2020","unstructured":"Demertzis K, Iliadis L, Tziritas N, Kikiras P (2020) Anomaly detection via blockchained deep learning smart contracts in industry 4.0. Neural Comput Appl 32(23):17361\u201317378. https:\/\/doi.org\/10.1007\/s00521-020-05189-8","journal-title":"Neural Comput Appl"},{"key":"7060_CR5","unstructured":"Chalapathy R, Chawla S (2019) Deep learning for anomaly detection: a survey. [Online]. Preprint http:\/\/arxiv.org\/abs\/1901.03407. Accessed 08 Sep 2021"},{"key":"7060_CR6","doi-asserted-by":"publisher","DOI":"10.3390\/iot2010009","author":"K Tsiknas","year":"2021","unstructured":"Tsiknas K, Taketzis D, Demertzis K, Skianis C (2021) Cyber threats to industrial IoT: a survey on attacks and countermeasures. IoT. https:\/\/doi.org\/10.3390\/iot2010009","journal-title":"IoT"},{"key":"7060_CR7","doi-asserted-by":"publisher","unstructured":"Deorankar AV, Thakare SS (2020) Survey on anomaly detection of (IoT)- Internet of Things cyberattacks using machine learning. In: 2020 fourth international conference on computing methodologies and communication (ICCMC), pp 115\u2013117. https:\/\/doi.org\/10.1109\/ICCMC48092.2020.ICCMC-00023","DOI":"10.1109\/ICCMC48092.2020.ICCMC-00023"},{"issue":"2","key":"7060_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","volume":"54","author":"G Pang","year":"2021","unstructured":"Pang G, Shen C, Cao L, van den Hengel A (2021) Deep learning for anomaly detection: a review. ACM Comput Surv 54(2):1\u201338. https:\/\/doi.org\/10.1145\/3439950","journal-title":"ACM Comput Surv"},{"key":"7060_CR9","doi-asserted-by":"publisher","unstructured":"Elmrabit N, Zhou F, Li F, Zhou H (2020) Evaluation of machine learning algorithms for anomaly detection. In: 2020 international conference on cyber security and protection of digital services (cyber security), pp 1\u20138. https:\/\/doi.org\/10.1109\/CyberSecurity49315.2020.9138871","DOI":"10.1109\/CyberSecurity49315.2020.9138871"},{"issue":"1","key":"7060_CR10","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1186\/s40537-020-00346-1","volume":"7","author":"K Al Jallad","year":"2020","unstructured":"Al Jallad K, Aljnidi M, Desouki MS (2020) Anomaly detection optimization using big data and deep learning to reduce false-positive. J Big Data 7(1):68. https:\/\/doi.org\/10.1186\/s40537-020-00346-1","journal-title":"J Big Data"},{"issue":"6","key":"7060_CR11","doi-asserted-by":"publisher","first-page":"4486","DOI":"10.1109\/JIOT.2018.2822842","volume":"5","author":"G Falco","year":"2018","unstructured":"Falco G, Caldera C, Shrobe H (2018) IIoT cybersecurity risk modeling for SCADA systems. IEEE Internet Things J 5(6):4486\u20134495. https:\/\/doi.org\/10.1109\/JIOT.2018.2822842","journal-title":"IEEE Internet Things J"},{"key":"7060_CR12","unstructured":"Gawlikowski J, et al. (2021) A Survey of uncertainty in deep neural Networks. [Online]. Preprint http:\/\/arxiv.org\/abs\/2107.03342. Accessed 06 Nov 2021"},{"key":"7060_CR13","doi-asserted-by":"publisher","unstructured":"Demertzis K, Iliadis L, Kikiras P (2021) A lipschitz-shapley explainable defense methodology against adversarial attacks. In: Artificial intelligence applications and innovations. AIAI 2021 IFIP WG 12.5 international workshops, Cham, pp 211\u2013227. https:\/\/doi.org\/10.1007\/978-3-030-79157-5_18","DOI":"10.1007\/978-3-030-79157-5_18"},{"key":"7060_CR14","doi-asserted-by":"publisher","unstructured":"Samrin R, Vasumathi D (2017) Review on anomaly based network intrusion detection system. In: 2017 international conference on electrical, electronics, communication, computer, and optimization techniques (ICEECCOT), pp 141\u2013147. https:\/\/doi.org\/10.1109\/ICEECCOT.2017.8284655","DOI":"10.1109\/ICEECCOT.2017.8284655"},{"key":"7060_CR15","unstructured":"Dias R, Alexandre L, Mauricio F, Poggi M (2020) Toward an efficient real-time anomaly detection system for cloud datacenters. In: 2020 IFIP networking conference (networking), pp 529\u2013533"},{"key":"7060_CR16","doi-asserted-by":"publisher","unstructured":"Feng T, Du Z, Sun Y, Wei J, Bi J, Liu J (2017) Real-time anomaly detection of short-time-scale GWAC survey light curves. In: 2017 IEEE international congress on big data (bigdata congress), pp 224\u2013231. https:\/\/doi.org\/10.1109\/BigDataCongress.2017.38","DOI":"10.1109\/BigDataCongress.2017.38"},{"key":"7060_CR17","doi-asserted-by":"publisher","unstructured":"Demertzis K, Iliadis L, Spartalis S (2017) A spiking one-class anomaly detection framework for cyber-security on industrial control systems. In: Engineering applications of neural networks, Cham, pp 122\u2013134. https:\/\/doi.org\/10.1007\/978-3-319-65172-9_11","DOI":"10.1007\/978-3-319-65172-9_11"},{"key":"7060_CR18","doi-asserted-by":"publisher","unstructured":"Kumar M, Mathur R (2014) Unsupervised outlier detection technique for intrusion detection in cloud computing. In: International conference for convergence for technology-2014, pp 1\u20134. https:\/\/doi.org\/10.1109\/I2CT.2014.7092027","DOI":"10.1109\/I2CT.2014.7092027"},{"key":"7060_CR19","doi-asserted-by":"publisher","unstructured":"MohanaPriya P, Shalinie SM (2017) Restricted Boltzmann machine based detection system for ddos attack in software defined networks. In: 2017 fourth international conference on signal processing, communication and networking (ICSCN), pp 1\u20136. https:\/\/doi.org\/10.1109\/ICSCN.2017.8085731","DOI":"10.1109\/ICSCN.2017.8085731"},{"issue":"8","key":"7060_CR20","doi-asserted-by":"publisher","first-page":"6187","DOI":"10.1109\/JIOT.2020.3034621","volume":"8","author":"X Xu","year":"2021","unstructured":"Xu X, Li J, Yang Y, Shen F (2021) Toward effective intrusion detection using log-cosh conditional variational autoencoder. IEEE Internet Things J 8(8):6187\u20136196. https:\/\/doi.org\/10.1109\/JIOT.2020.3034621","journal-title":"IEEE Internet Things J"},{"key":"7060_CR21","doi-asserted-by":"publisher","first-page":"108346","DOI":"10.1109\/ACCESS.2020.3001350","volume":"8","author":"S Zavrak","year":"2020","unstructured":"Zavrak S, \u0130skefiyeli M (2020) Anomaly-based intrusion detection from network flow features using variational autoencoder. IEEE Access 8:108346\u2013108358. https:\/\/doi.org\/10.1109\/ACCESS.2020.3001350","journal-title":"IEEE Access"},{"key":"7060_CR22","unstructured":"Doersch C (2021) Tutorial on variational autoencoders, [Online]. Preprint http:\/\/arxiv.org\/abs\/1606.05908. Accessed 09 Sep 2021"},{"key":"7060_CR23","doi-asserted-by":"publisher","unstructured":"Kim J, Sim A, Kim J, Wu K (2020) Botnet detection using recurrent variational autoencoder. In: GLOBECOM 2020-2020 IEEE global communications conference, pp 1\u20136. https:\/\/doi.org\/10.1109\/GLOBECOM42002.2020.9348169","DOI":"10.1109\/GLOBECOM42002.2020.9348169"},{"key":"7060_CR24","doi-asserted-by":"publisher","unstructured":"Gu Z, Yang Y (2021) Detecting malicious model updates from federated learning on conditional variational autoencoder. In: 2021 IEEE international parallel and distributed processing symposium (IPDPS), pp 671\u2013680. https:\/\/doi.org\/10.1109\/IPDPS49936.2021.00075","DOI":"10.1109\/IPDPS49936.2021.00075"},{"key":"7060_CR25","unstructured":"Farooq MJ, Zhu Q (2019) IoT supply chain security: overview, challenges, and the road ahead, [Online]. Preprint http:\/\/arxiv.org\/abs\/1908.07828. Accessed 19 Jan 2021"},{"issue":"1","key":"7060_CR26","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1109\/JSYST.2019.2906120","volume":"14","author":"H Li","year":"2020","unstructured":"Li H, Zhou S, Yuan W, Li J, Leung H (2020) Adversarial-Example attacks toward android malware detection system. IEEE Syst J 14(1):653\u2013656. https:\/\/doi.org\/10.1109\/JSYST.2019.2906120","journal-title":"IEEE Syst J"},{"key":"7060_CR27","doi-asserted-by":"publisher","unstructured":"Liu Y, Mao S, Mei X, Yang T, Zhao X (2019) Sensitivity of adversarial perturbation in fast gradient sign method. In: 2019 IEEE symposium series on computational intelligence (SSCI), pp 433\u2013436. https:\/\/doi.org\/10.1109\/SSCI44817.2019.9002856","DOI":"10.1109\/SSCI44817.2019.9002856"},{"key":"7060_CR28","doi-asserted-by":"publisher","unstructured":"Hwang W-S, Yun J-H, Kim J, Kim HC (2019) Time-series aware precision and recall for anomaly detection: considering variety of detection result and addressing ambiguous labeling. In: Proceedings of the 28th ACM international conference on information and knowledge management, New York, pp 2241\u20132244. https:\/\/doi.org\/10.1145\/3357384.3358118","DOI":"10.1145\/3357384.3358118"},{"key":"7060_CR29","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3123586","author":"P Tang","year":"2021","unstructured":"Tang P, Wang W, Lou J, Xiong L (2021) Generating adversarial examples with distance constrained adversarial imitation networks. IEEE Trans Dependable Secure Comput. https:\/\/doi.org\/10.1109\/TDSC.2021.3123586","journal-title":"IEEE Trans Dependable Secure Comput"},{"key":"7060_CR30","doi-asserted-by":"publisher","unstructured":"Yu P, Song K, Lu J (2018) Generating adversarial examples with conditional generative adversarial net. In: 2018 24th international conference on pattern recognition (ICPR), pp 676\u2013681. https:\/\/doi.org\/10.1109\/ICPR.2018.8545152","DOI":"10.1109\/ICPR.2018.8545152"},{"issue":"6","key":"7060_CR31","doi-asserted-by":"publisher","first-page":"800","DOI":"10.26599\/TST.2020.9010038","volume":"26","author":"K Han","year":"2021","unstructured":"Han K, Li Y, Xia B (2021) A cascade model-aware generative adversarial example detection method. Tsinghua Sci Technol 26(6):800\u2013812. https:\/\/doi.org\/10.26599\/TST.2020.9010038","journal-title":"Tsinghua Sci Technol"},{"key":"7060_CR32","doi-asserted-by":"publisher","first-page":"126582","DOI":"10.1109\/ACCESS.2019.2939352","volume":"7","author":"U Hwang","year":"2019","unstructured":"Hwang U, Park J, Jang H, Yoon S, Cho NI (2019) PuVAE: a variational autoencoder to purify adversarial examples. IEEE Access 7:126582\u2013126593. https:\/\/doi.org\/10.1109\/ACCESS.2019.2939352","journal-title":"IEEE Access"},{"key":"7060_CR33","doi-asserted-by":"publisher","unstructured":"Austin J, Kennedy J, Lees K (1995) A neural architecture for fast rule matching. In: Proceedings 1995 second new zealand international two-stream conference on artificial neural networks and expert systems, pp 255\u2013260. https:\/\/doi.org\/10.1109\/ANNES.1995.499484","DOI":"10.1109\/ANNES.1995.499484"},{"issue":"11","key":"7060_CR34","doi-asserted-by":"publisher","first-page":"6699","DOI":"10.1007\/s00521-019-04288-5","volume":"32","author":"L Xing","year":"2020","unstructured":"Xing L, Demertzis K, Yang J (2020) Identifying data streams anomalies by evolving spiking restricted Boltzmann machines. Neural Comput Appl 32(11):6699\u20136713. https:\/\/doi.org\/10.1007\/s00521-019-04288-5","journal-title":"Neural Comput Appl"},{"issue":"4","key":"7060_CR35","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1007\/s10489-015-0678-6","volume":"43","author":"AB Mrad","year":"2015","unstructured":"Mrad AB, Delcroix V, Piechowiak S, Leicester P, Abid M (2015) An explication of uncertain evidence in bayesian networks: likelihood evidence and probabilistic evidence. Appl Intell 43(4):802\u2013824. https:\/\/doi.org\/10.1007\/s10489-015-0678-6","journal-title":"Appl Intell"},{"key":"7060_CR36","doi-asserted-by":"publisher","unstructured":"De La Rosa E, Yu W (2015) Restricted Boltzmann machine for nonlinear system modeling. In: 2015 IEEE 14th International conference on machine learning and applications (ICMLA), pp 443\u2013446. https:\/\/doi.org\/10.1109\/ICMLA.2015.24","DOI":"10.1109\/ICMLA.2015.24"},{"key":"7060_CR37","unstructured":"Marlin B, Swersky K, Chen B, Freitas N (2010) Inductive principles for restricted Boltzmann machine learning. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 509\u2013516. [Online]. https:\/\/proceedings.mlr.press\/v9\/marlin10a.html. Accessed 10 Sep 2021"},{"key":"7060_CR38","doi-asserted-by":"publisher","DOI":"10.1038\/s43586-020-00001-2","author":"R van de Schoot","year":"2021","unstructured":"van de Schoot R et al (2021) Bayesian statistics and modelling. Nat Rev Methods Primer. https:\/\/doi.org\/10.1038\/s43586-020-00001-2","journal-title":"Nat Rev Methods Primer"},{"key":"7060_CR39","unstructured":"Kingma DP, Welling M (2014) Auto-encoding variational bayes. [Online]. Preprint http:\/\/arxiv.org\/abs\/1312.6114. Accessed 09 Sep 2021"},{"issue":"8","key":"7060_CR40","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798\u20131828. https:\/\/doi.org\/10.1109\/TPAMI.2013.50","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"7060_CR41","doi-asserted-by":"publisher","first-page":"197","DOI":"10.3233\/ICA-2010-0345","volume":"17","author":"M Ahmadlou","year":"2010","unstructured":"Ahmadlou M, Adeli H (2010) Enhanced probabilistic neural network with local decision circles: a robust classifier. Integr Comput Aided Eng 17(3):197\u2013210","journal-title":"Integr Comput Aided Eng"},{"issue":"2","key":"7060_CR42","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/0010-0285(73)90033-9","volume":"5","author":"A Tversky","year":"1973","unstructured":"Tversky A, Kahneman D (1973) Availability: a heuristic for judging frequency and probability. Cognit Psychol 5(2):207\u2013232. https:\/\/doi.org\/10.1016\/0010-0285(73)90033-9","journal-title":"Cognit Psychol"},{"key":"7060_CR43","volume-title":"The elements of statistical learning: data mining, inference, and prediction, second edition","author":"T Hastie","year":"2016","unstructured":"Hastie T, Tibshirani R, Friedman J (2016) The elements of statistical learning: data mining, inference, and prediction, second edition, 2nd edn. Springer, New York","edition":"2"},{"key":"7060_CR44","doi-asserted-by":"publisher","unstructured":"Barshan E, Fieguth P (2014) Scalable learning for restricted Boltzmann machines. In: 2014 IEEE international conference on image processing (ICIP), pp 2754\u20132758. https:\/\/doi.org\/10.1109\/ICIP.2014.7025557","DOI":"10.1109\/ICIP.2014.7025557"},{"key":"7060_CR45","doi-asserted-by":"publisher","unstructured":"Chen L, Zou W (2018) Improvement of restricted Boltzmann machine by sparse representation based on lorentz function. In: 2018 7th international congress on advanced applied informatics (IIAI-AAI), pp 968\u2013969. https:\/\/doi.org\/10.1109\/IIAI-AAI.2018.00205","DOI":"10.1109\/IIAI-AAI.2018.00205"},{"key":"7060_CR46","doi-asserted-by":"publisher","unstructured":"Yu J, Gwak J, Lee S, Jeon M (2015) An incremental learning approach for restricted Boltzmann machines. In 2015 International conference on control, automation and information sciences (ICCAIS), pp 113\u2013117. https:\/\/doi.org\/10.1109\/ICCAIS.2015.7338643","DOI":"10.1109\/ICCAIS.2015.7338643"},{"key":"7060_CR47","doi-asserted-by":"publisher","unstructured":"He T, Luo X, Liu Z (2017) A probabilistic indoor localization algorithm based on Restricted Boltzmann Machine. In 2017 IEEE 2nd advanced information technology, electronic and automation control conference (IAEAC), pp 1364\u20131368. https:\/\/doi.org\/10.1109\/IAEAC.2017.8054237","DOI":"10.1109\/IAEAC.2017.8054237"},{"issue":"12","key":"7060_CR48","doi-asserted-by":"publisher","first-page":"8675","DOI":"10.1007\/s00521-019-04359-7","volume":"32","author":"KMdR Alam","year":"2020","unstructured":"Alam KMdR, Siddique N, Adeli H (2020) A dynamic ensemble learning algorithm for neural networks. Neural Comput Appl 32(12):8675\u20138690. https:\/\/doi.org\/10.1007\/s00521-019-04359-7","journal-title":"Neural Comput Appl"},{"key":"7060_CR49","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM (2006) Pattern recognition and machine learning. Springer, New York"},{"issue":"3\u20135","key":"7060_CR50","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.physrep.2009.11.002","volume":"486","author":"S Fortunato","year":"2010","unstructured":"Fortunato S (2010) Community detection in graphs. Phys Rep 486(3\u20135):75\u2013174. https:\/\/doi.org\/10.1016\/j.physrep.2009.11.002","journal-title":"Phys Rep"},{"issue":"13","key":"7060_CR51","doi-asserted-by":"publisher","first-page":"4972","DOI":"10.1073\/pnas.0709247105","volume":"105","author":"L Lacasa","year":"2008","unstructured":"Lacasa L, Luque B, Ballesteros F, Luque J, Nu\u00f1o JC (2008) From time series to complex networks: the visibility graph. Proc Natl Acad Sci 105(13):4972\u20134975. https:\/\/doi.org\/10.1073\/pnas.0709247105","journal-title":"Proc Natl Acad Sci"},{"issue":"1\u20134","key":"7060_CR52","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1093\/lpr\/mgm014","volume":"6","author":"JL Pollock","year":"2007","unstructured":"Pollock JL (2007) Reasoning and probability. Law Probab Risk 6(1\u20134):43\u201358. https:\/\/doi.org\/10.1093\/lpr\/mgm014","journal-title":"Law Probab Risk"},{"issue":"2\u20133","key":"7060_CR53","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1093\/lpr\/mgs004","volume":"11","author":"D Hamer","year":"2012","unstructured":"Hamer D (2012) Probability, anti-resilience, and the weight of expectation. Law Probab Risk 11(2\u20133):135\u2013158. https:\/\/doi.org\/10.1093\/lpr\/mgs004","journal-title":"Law Probab Risk"},{"key":"7060_CR54","unstructured":"Bengio Y, Courville A, Vincent P (2014) Representation Learning: a review and new perspectives. [Online]. Preprint http:\/\/arxiv.org\/abs\/1206.5538. Accessed 09 Sep 2021"},{"issue":"1","key":"7060_CR55","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1162\/evco_a_00242","volume":"27","author":"P Kerschke","year":"2019","unstructured":"Kerschke P, Hoos HH, Neumann F, Trautmann H (2019) Automated algorithm selection: survey and perspectives. Evol Comput 27(1):3\u201345. https:\/\/doi.org\/10.1162\/evco_a_00242","journal-title":"Evol Comput"},{"key":"7060_CR56","doi-asserted-by":"publisher","unstructured":"Calvayrac F (2015) Kullback-Leibler divergence as an estimate of reproducibility of numerical results. In: 2015 7th international conference on new technologies, mobility and security (NTMS), pp 1\u20135. https:\/\/doi.org\/10.1109\/NTMS.2015.7266501","DOI":"10.1109\/NTMS.2015.7266501"},{"key":"7060_CR57","doi-asserted-by":"publisher","unstructured":"Liu J-W, Chi G-H, Luo X-L (2013) Contrastive divergence learning for the restricted Boltzmann machine. In: 2013 Ninth international conference on natural computation (ICNC), pp 18\u201322. https:\/\/doi.org\/10.1109\/ICNC.2013.6817936","DOI":"10.1109\/ICNC.2013.6817936"},{"key":"7060_CR58","unstructured":"Yildirim I, Bayesian inference: gibbs sampling, p 6"},{"key":"7060_CR59","unstructured":"Zhang C, Butepage J, Kjellstrom H, Mandt S (2018) Advances in variational inference [Online]. Preprint http:\/\/arxiv.org\/abs\/1711.05597. Accessed 08 Sep 2021"},{"key":"7060_CR60","unstructured":"Gregor K, Papamakarios G, Besse F, Buesing L, Weber T (2019) Temporal difference variational auto-encoder. [Online]. Preprint http:\/\/arxiv.org\/abs\/1806.03107. Accessed 08 Sep 2021"},{"key":"7060_CR61","doi-asserted-by":"publisher","unstructured":"Shekhar S, Xiong H, Zhou X (eds) (2017) Euclidean distance. In: Encyclopedia of GIS, Springer, Cham, p 556. https:\/\/doi.org\/10.1007\/978-3-319-17885-1_100372","DOI":"10.1007\/978-3-319-17885-1_100372"},{"key":"7060_CR62","doi-asserted-by":"publisher","unstructured":"Xue Y. Zhang L, Wang B, Li F (2019) feature selection based on the kullback-leibler distance and its application on fault diagnosis. In: 2019 seventh international conference on advanced cloud and big data (CBD), pp 246\u2013251. https:\/\/doi.org\/10.1109\/CBD.2019.00052","DOI":"10.1109\/CBD.2019.00052"},{"issue":"2","key":"7060_CR63","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1109\/TAFFC.2017.2695999","volume":"10","author":"DH Kim","year":"2019","unstructured":"Kim DH, Baddar WJ, Jang J, Ro YM (2019) Multi-objective based spatio-temporal feature representation learning robust to expression intensity variations for facial expression recognition. IEEE Trans Affect Comput 10(2):223\u2013236. https:\/\/doi.org\/10.1109\/TAFFC.2017.2695999","journal-title":"IEEE Trans Affect Comput"},{"key":"7060_CR64","unstructured":"Kucukelbir A, Tran D, Ranganath R, Gelman A, Blei DM (2016) Automatic differentiation variational inference. [Online]. Preprint http:\/\/arxiv.org\/abs\/1603.00788. Accessed 10 Sep 2021"},{"key":"7060_CR65","unstructured":"Shin H-K, Lee W, Yun J-H, Kim H (2020) {HAI} 1.0: HIL-based augmented {ICS} security dataset, presented at the 13th {USENIX} workshop on cyber security experimentation and test ({CSET} 20), [Online]. https:\/\/www.usenix.org\/conference\/cset20\/presentation\/shin. Accessed 10 Sep 2021"},{"key":"7060_CR66","doi-asserted-by":"publisher","unstructured":"Shin H-K, Lee W, Yun J-H, Min B-G (2021) Two ICS security datasets and anomaly detection contest on the HIL-based augmented ICS testbed. In: Cyber security experimentation and test workshop, Virtual CA USA, pp 36\u201340. https:\/\/doi.org\/10.1145\/3474718.3474719","DOI":"10.1145\/3474718.3474719"},{"key":"7060_CR67","doi-asserted-by":"publisher","unstructured":"Choi S, Yun J-H, Kim S-K (2019) A comparison of ICS datasets for security research based on attack paths. In: Critical information infrastructures security, Springer, Cham, pp 154\u2013166. https:\/\/doi.org\/10.1007\/978-3-030-05849-4_12","DOI":"10.1007\/978-3-030-05849-4_12"},{"key":"7060_CR68","doi-asserted-by":"crossref","unstructured":"Wood SN (2015) Core statistics. In: Cambridge core, https:\/\/www.cambridge.org\/core\/books\/core-statistics\/F303F4463E162C6534641616AE38C0A6 Accessed 29 Jun 2021","DOI":"10.1017\/CBO9781107741973"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07060-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07060-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07060-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T18:15:18Z","timestamp":1662056118000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07060-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,1]]},"references-count":68,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["7060"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07060-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,3,1]]},"assertion":[{"value":"11 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}