{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,13]],"date-time":"2025-04-13T00:53:48Z","timestamp":1744505628524},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Fusion"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1016\/j.inffus.2022.11.024","type":"journal-article","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T14:49:20Z","timestamp":1669387760000},"page":"115-126","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":8,"special_numbering":"C","title":["Evidential classification for defending against adversarial attacks on network traffic"],"prefix":"10.1016","volume":"92","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-7683-3867","authenticated-orcid":false,"given":"Matthew","family":"Beechey","sequence":"first","affiliation":[]},{"given":"Sangarapillai","family":"Lambotharan","sequence":"additional","affiliation":[]},{"given":"Konstantinos G.","family":"Kyriakopoulos","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.inffus.2022.11.024_b1","series-title":"Introduction to Machine Learning","author":"Alpaydin","year":"2010"},{"issue":"2","key":"10.1016\/j.inffus.2022.11.024_b2","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/nbt1386","article-title":"What are artificial neural networks?","volume":"26","author":"Krogh","year":"2008","journal-title":"Nature Biotechnol."},{"key":"10.1016\/j.inffus.2022.11.024_b3","doi-asserted-by":"crossref","first-page":"134480","DOI":"10.1109\/ACCESS.2020.3011293","article-title":"Learning to learn sequential network attacks using hidden Markov models","volume":"8","author":"Chadza","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.inffus.2022.11.024_b4","doi-asserted-by":"crossref","first-page":"99508","DOI":"10.1109\/ACCESS.2019.2930200","article-title":"Hidden Markov models and alert correlations for the prediction of advanced persistent threats","volume":"7","author":"Ghafir","year":"2019","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.inffus.2022.11.024_b5","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1109\/TDSC.2017.2751478","article-title":"Real-time multistep attack prediction based on hidden Markov models","volume":"17","author":"Holgado","year":"2017","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"10.1016\/j.inffus.2022.11.024_b6","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.procs.2018.10.315","article-title":"Adversarial attacks and defenses against deep neural networks: A survey","volume":"140","author":"Ozdag","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.inffus.2022.11.024_b7","series-title":"International Conference on Learning Representations (ICLR)","first-page":"1","article-title":"Deep evidential regression","author":"Amini","year":"2019"},{"key":"10.1016\/j.inffus.2022.11.024_b8","first-page":"3179","article-title":"Evidential deep learning to quantify classification uncertainty","author":"Sensoy","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.inffus.2022.11.024_b9","series-title":"A Mathematical Theory of Evidence","author":"Shafer","year":"1976"},{"key":"10.1016\/j.inffus.2022.11.024_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107120","article-title":"Evidential classification and feature selection for cyber-threat hunting","volume":"226","author":"Beechey","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.inffus.2022.11.024_b11","doi-asserted-by":"crossref","unstructured":"S.-M. Moosavi-Dezfooli, A. Fawzi, P. Frossard, Deepfool: a simple and accurate method to fool deep neural networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2574\u20132582.","DOI":"10.1109\/CVPR.2016.282"},{"key":"10.1016\/j.inffus.2022.11.024_b12","series-title":"Adversarial machine learning at scale","author":"Kurakin","year":"2016"},{"key":"10.1016\/j.inffus.2022.11.024_b13","series-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"248","article-title":"Imagenet: A large-scale hierarchical image database","author":"Deng","year":"2009"},{"key":"10.1016\/j.inffus.2022.11.024_b14","series-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2014"},{"key":"10.1016\/j.inffus.2022.11.024_b15","series-title":"Recent advances in adversarial training for adversarial robustness","author":"Bai","year":"2021"},{"key":"10.1016\/j.inffus.2022.11.024_b16","series-title":"Learning with a strong adversary","author":"Huang","year":"2016"},{"key":"10.1016\/j.inffus.2022.11.024_b17","series-title":"Ensemble adversarial training: Attacks and defenses","author":"Tram\u00e8r","year":"2017"},{"key":"10.1016\/j.inffus.2022.11.024_b18","series-title":"2016 IEEE Symposium on Security and Privacy","first-page":"582","article-title":"Distillation as a defense to adversarial perturbations against deep neural networks","author":"Papernot","year":"2016"},{"key":"10.1016\/j.inffus.2022.11.024_b19","series-title":"On the effectiveness of defensive distillation","author":"Papernot","year":"2016"},{"key":"10.1016\/j.inffus.2022.11.024_b20","series-title":"Computer Security","first-page":"62","article-title":"Adversarial examples for malware detection","author":"Grosse","year":"2017"},{"key":"10.1016\/j.inffus.2022.11.024_b21","series-title":"Defensive distillation is not robust to adversarial examples","author":"Carlini","year":"2016"},{"key":"10.1016\/j.inffus.2022.11.024_b22","series-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN: Image Processing","first-page":"685","article-title":"Evaluating defensive distillation for defending text processing neural networks against adversarial examples","author":"Soll","year":"2019"},{"key":"10.1016\/j.inffus.2022.11.024_b23","doi-asserted-by":"crossref","unstructured":"J. Lu, T. Issaranon, D. Forsyth, Safetynet: Detecting and rejecting adversarial examples robustly, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 446\u2013454.","DOI":"10.1109\/ICCV.2017.56"},{"key":"10.1016\/j.inffus.2022.11.024_b24","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.knosys.2019.03.030","article-title":"Logistic regression, neural networks and Dempster-Shafer theory: A new perspective","volume":"176","author":"Den\u0153ux","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.inffus.2022.11.024_b25","series-title":"Deep learning using rectified linear units (relu)","author":"Agarap","year":"2018"},{"key":"10.1016\/j.inffus.2022.11.024_b26","series-title":"Activation functions: Comparison of trends in practice and research for deep learning","author":"Nwankpa","year":"2018"},{"key":"10.1016\/j.inffus.2022.11.024_b27","series-title":"On the (statistical) detection of adversarial examples","author":"Grosse","year":"2017"},{"key":"10.1016\/j.inffus.2022.11.024_b28","series-title":"Towards robust detection of adversarial examples","author":"Pang","year":"2017"},{"key":"10.1016\/j.inffus.2022.11.024_b29","series-title":"Pixeldefend: Leveraging generative models to understand and defend against adversarial examples","author":"Song","year":"2017"},{"key":"10.1016\/j.inffus.2022.11.024_b30","series-title":"Belief functions: Theory and algorithms","author":"Reineking","year":"2014"},{"key":"10.1016\/j.inffus.2022.11.024_b31","series-title":"2008 16th Mediterranean Conference on Control and Automation","first-page":"603","article-title":"Dynamic evidential networks in system reliability analysis: A Dempster Shafer approach","author":"Weber","year":"2008"},{"key":"10.1016\/j.inffus.2022.11.024_b32","series-title":"Non-Standard Logics for Automated Reasoning","author":"Smets","year":"1988"},{"key":"10.1016\/j.inffus.2022.11.024_b33","doi-asserted-by":"crossref","first-page":"40008","DOI":"10.1109\/ACCESS.2018.2855078","article-title":"A basic probability assignment methodology for unsupervised wireless intrusion detection","volume":"6","author":"Ghafir","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/j.inffus.2022.11.024_b34","series-title":"Encyclopedia of Ecology","first-page":"307","article-title":"Decision analysis","author":"Borsuk","year":"2008"},{"issue":"5","key":"10.1016\/j.inffus.2022.11.024_b35","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1287\/opre.30.5.803","article-title":"Decision analysis: An overview","volume":"30","author":"Keeney","year":"1982","journal-title":"Oper. Res."},{"issue":"4","key":"10.1016\/j.inffus.2022.11.024_b36","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1037\/h0053870","article-title":"The theory of decision making","volume":"51","author":"Edwards","year":"1954","journal-title":"Psychol. Bull."},{"key":"10.1016\/j.inffus.2022.11.024_b37","series-title":"The threat of adversarial attacks on machine learning in network security\u2013a survey","author":"Ibitoye","year":"2019"},{"key":"10.1016\/j.inffus.2022.11.024_b38","series-title":"Adversarial robustness toolbox v1.2.0","author":"Nicolae","year":"2018"},{"issue":"2","key":"10.1016\/j.inffus.2022.11.024_b39","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s11633-019-1211-x","article-title":"Adversarial attacks and defenses in images, graphs and text: A review","volume":"17","author":"Xu","year":"2020","journal-title":"Int. J. Autom. Comput."},{"key":"10.1016\/j.inffus.2022.11.024_b40","series-title":"CSE-CIC-IDS2018 on AWS","year":"2018"},{"key":"10.1016\/j.inffus.2022.11.024_b41","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.procs.2016.05.161","article-title":"Detection and mitigation of denial of service attacks using stratified architecture","volume":"87","author":"Prakash","year":"2016","journal-title":"Procedia Comput. Sci."},{"issue":"3","key":"10.1016\/j.inffus.2022.11.024_b42","first-page":"118","article-title":"A review on statistical approaches for anomaly detection in ddos attacks","volume":"29","author":"Nooribakhsh","year":"2020","journal-title":"Inf. Secur. J.: Glob. Perspect."},{"issue":"2","key":"10.1016\/j.inffus.2022.11.024_b43","doi-asserted-by":"crossref","first-page":"7","DOI":"10.14302\/issn.2692-5915.jafs-20-3601","article-title":"A critical evaluation of the estonian cyber incident","volume":"1","author":"Buresh\u00a0Ph.D.","year":"2020","journal-title":"J. Adv. Forensic Sci."},{"key":"10.1016\/j.inffus.2022.11.024_b44","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.inffus.2022.11.024_b45","series-title":"Neural Networks: Tricks of the Trade","first-page":"9","article-title":"Efficient backprop","author":"LeCun","year":"2012"},{"key":"10.1016\/j.inffus.2022.11.024_b46","series-title":"Network Anomaly Detection: A Machine Learning Perspective","author":"Bhattacharyya","year":"2013"},{"key":"10.1016\/j.inffus.2022.11.024_b47","series-title":"Macro F1 and macro F1","author":"Opitz","year":"2019"}],"container-title":["Information Fusion"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1566253522002342?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1566253522002342?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T05:55:52Z","timestamp":1673848552000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1566253522002342"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4]]},"references-count":47,"alternative-id":["S1566253522002342"],"URL":"https:\/\/doi.org\/10.1016\/j.inffus.2022.11.024","relation":{},"ISSN":["1566-2535"],"issn-type":[{"value":"1566-2535","type":"print"}],"subject":[],"published":{"date-parts":[[2023,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Evidential classification for defending against adversarial attacks on network traffic","name":"articletitle","label":"Article Title"},{"value":"Information Fusion","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.inffus.2022.11.024","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}