{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:45:02Z","timestamp":1777704302410,"version":"3.51.4"},"reference-count":60,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2018,8,17]],"date-time":"2018-08-17T00:00:00Z","timestamp":1534464000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,10]]},"abstract":"<jats:p>Machine learning is successful in many applications including securing a network from unseen attack. The application of learning algorithm for detecting anomaly in a Network has been fundamental since few years. With increasing use of machine learning techniques it has become important to study to what extent it is good to be dependent on them. Altogether a different discipline called \u2018Adversarial Learning\u2019 have come up as a separate dimension of study. The work in this paper is to test the robustness of online machine learning based IDS to carefully crafted packets by attacker called poison packets. The objective is to observe how a remote attacker can deviate the normal behavior of machine learning based classifier in the IDS by injecting the network with carefully crafted packets externally, that may seem normal by the classification algorithm and the instance made part of its future training set. This behavior eventually can lead to a poison learning by the classification algorithm in the long run, resulting in misclassification of true attack instances. This work explores one such approach with SOM and SVM as the online learning based classification algorithms.<\/jats:p>","DOI":"10.3233\/jifs-18202","type":"journal-article","created":{"date-parts":[[2018,8,17]],"date-time":"2018-08-17T11:22:42Z","timestamp":1534504962000},"page":"3635-3651","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Are machine learning based intrusion detection system always secure? An insight into tampered learning"],"prefix":"10.1177","volume":"35","author":[{"given":"Rupam Kumar","family":"Sharma","sequence":"first","affiliation":[{"name":"Department of Information Technology, NEHU, Shillong, Meghalaya, India"}]},{"given":"Hemanta Kr","family":"Kalita","sequence":"additional","affiliation":[{"name":"Department of Information Technology, NEHU, Shillong, Meghalaya, India"}]},{"given":"Biju","family":"Issac","sequence":"additional","affiliation":[{"name":"School of Computing, Media &amp; Arts, Teesside University, U.K."}]}],"member":"179","published-online":{"date-parts":[[2018,8,17]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.05.058"},{"key":"e_1_3_1_3_2","unstructured":"TavallaeeM. et al Nsl-kdd dataset (2012) http:\/\/www.iscx.ca\/NSL-KDD."},{"key":"e_1_3_1_4_2","first-page":"156","article-title":"Multilayer perceptrons","volume":"2","author":"Haykin S.","year":"1999","unstructured":"HaykinS., Multilayer perceptrons, Neural Networks: A Comprehensive Foundation 2 (1999), 156\u2013255.","journal-title":"Neural Networks: A Comprehensive Foundation"},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","unstructured":"HaWangS.-C. Artificial neural network Interdisciplinary Computing in Java Programming Springer US (2003) pp. 81\u2013100.","DOI":"10.1007\/978-1-4615-0377-4_5"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2002.1007774"},{"key":"e_1_3_1_7_2","unstructured":"MeyerD. and WienF.H.T. Support vector machines The Interface to libsvm in package e1071 2015."},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1002\/wics.49"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-78293-3_17"},{"key":"e_1_3_1_10_2","first-page":"701","article-title":"Learning from labeled and unlabeled data","volume":"10","author":"Mitchell T.M.","year":"2006","unstructured":"MitchellT.M., Learning from labeled and unlabeled data, Machine Learning 10 (2006), 701.","journal-title":"Machine Learning"},{"key":"e_1_3_1_11_2","unstructured":"MurphyK.P. Naive bayes classifiers University of British Columbia 2006."},{"key":"e_1_3_1_12_2","unstructured":"ZamaniM. and MahnushM. Machine Learning Techniques for Intrusion Detection arXiv preprint arXiv:1312.2177 2013."},{"key":"e_1_3_1_13_2","unstructured":"OrrM.J.L. Introduction to radial basis function networks 1996."},{"issue":"4","key":"e_1_3_1_14_2","first-page":"3","article-title":"K-means clustering tutorial","volume":"100","author":"Teknomo K.","year":"2006","unstructured":"TeknomoK., K-means clustering tutorial, Medicine 100(4) (2006), 3.","journal-title":"Medicine"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.02.102"},{"key":"e_1_3_1_16_2","doi-asserted-by":"crossref","unstructured":"AhmadI. AbdullahA.B. and AlghamdiA.S. Application of artificial neural network in detection of DOS attacks Proceedings of the 2nd International Conference on Security of Information and Networks ACM 2009.","DOI":"10.1145\/1626195.1626252"},{"key":"e_1_3_1_17_2","article-title":"Classifying attacks in a network intrusion detection system based on artificial neural networks","author":"Norouzian M.R.","year":"2011","unstructured":"NorouzianM.R. and MeratiS., Classifying attacks in a network intrusion detection system based on artificial neural networks, Advanced Communication Technology (ICACT), 2011 13th International Conference on IEEE, 2011.","journal-title":"Advanced Communication Technology (ICACT), 2011 13th International Conference on IEEE"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.06.066"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.07.032"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACIIDS.2009.59"},{"key":"e_1_3_1_21_2","article-title":"Network traffic anomaly detection based on growing hierarchical SOM","author":"Huang S.-Y.","year":"2013","unstructured":"HuangS.-Y. and HuangY.-N., Network traffic anomaly detection based on growing hierarchical SOM, 2013 43rd Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN) IEEE, 2013.","journal-title":"2013 43rd Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN) IEEE"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2012.09.004"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-010-0487-0"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.06.013"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2012.05.004"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2012.01.849"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.07.009"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2011.10.001"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.protcy.2012.05.017"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1002\/nem.804"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2011.12.004"},{"key":"e_1_3_1_32_2","article-title":"A fuzzy logic based reputation model against unfair ratings","author":"Liu S.","year":"2013","unstructured":"LiuS. et al., A fuzzy logic based reputation model against unfair ratings, Proceedings of the 2013 International Conference on Autonomous Agents and Multi-Agent Systems, International Foundation for Autonomous Agents and Multiagent Systems, 2013.","journal-title":"Proceedings of the 2013 International Conference on Autonomous Agents and Multi-Agent Systems, International Foundation for Autonomous Agents and Multiagent Systems"},{"key":"e_1_3_1_33_2","article-title":"Intrusion detection using an ensemble of classification methods","volume":"1","author":"Govindarajan M.","year":"2012","unstructured":"GovindarajanM. and ChandrasekaranR.M., Intrusion detection using an ensemble of classification methods, World Congress on Engineering and Computer Science 1 2012.","journal-title":"World Congress on Engineering and Computer Science"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2012.6252449"},{"issue":"1","key":"e_1_3_1_35_2","article-title":"A new network intrusion detection algorithm based on radial basis function neural networks classifier","volume":"4","author":"Hongqiang J.","year":"2012","unstructured":"HongqiangJ., LiminJ. and YanhuaJ., A new network intrusion detection algorithm based on radial basis function neural networks classifier, Advances in Information Sciences & Service Sciences 4(1) (2012).","journal-title":"Advances in Information Sciences & Service Sciences"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.07.032"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.01.009"},{"key":"e_1_3_1_38_2","article-title":"An improved network intrusion detection technique based on k-means clustering via Na\u00efve bayes classification","author":"Sharma S.K.","year":"2012","unstructured":"SharmaS.K. et al., An improved network intrusion detection technique based on k-means clustering via Na\u00efve bayes classification, Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on, IEEE, 2012.","journal-title":"Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on, IEEE"},{"key":"e_1_3_1_39_2","unstructured":"HettichS. BayS.D. The UCI KDD Archive 1999. http:\/\/kdd.ics.uci.edu. Irvine CA: University of California Department of Information and Computer Science."},{"key":"e_1_3_1_40_2","article-title":"A Detailed Analysis of the KDD CUP 99 Data Set","author":"Tavallaee M.","year":"2009","unstructured":"TavallaeeM., BagheriE., LuW. and GhorbaniA., A Detailed Analysis of the KDD CUP 99 Data Set, Submitted to Second IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), 2009.","journal-title":"Submitted to Second IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA)"},{"key":"e_1_3_1_41_2","doi-asserted-by":"crossref","unstructured":"HuangL. et al. Adversarial machine learning Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence ACM 2011.","DOI":"10.1145\/2046684.2046692"},{"key":"e_1_3_1_42_2","doi-asserted-by":"crossref","unstructured":"BarrenoM. et al. Can machine learning be secure? Proceedings of the 2006 ACM Symposium on Information Computer and Communications Security ACM 2006.","DOI":"10.1145\/1128817.1128824"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1002\/sec.341"},{"key":"e_1_3_1_44_2","unstructured":"RanjanS. and ChenF. Machine learning based botnet detection with dynamic adaptation U.S. Patent No. 8 402 543 2013."},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.01.009"},{"key":"e_1_3_1_46_2","article-title":"Bayesian model averaging of bayesian network classifiers for intrusion detection","author":"Xiao L.","year":"2014","unstructured":"XiaoL., ChenY. and ChangC.K., Bayesian model averaging of bayesian network classifiers for intrusion detection, Computer Software and Applications Conference Workshops (COMPSACW), 2014 IEEE 38th International IEEE, 2014.","journal-title":"Computer Software and Applications Conference Workshops (COMPSACW), 2014 IEEE 38th International IEEE"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.05.058"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-81-322-2529-4_51"},{"key":"e_1_3_1_49_2","article-title":"Plant based biologically inspired intrusion response mechanism: An insight into the proposed model PIRIDS","author":"Sharma R.K.","year":"2016","unstructured":"SharmaR.K., KalitaH.K. and IssacB., Plant based biologically inspired intrusion response mechanism: An insight into the proposed model PIRIDS, Journal of Information Assurance and Security (2016).","journal-title":"Journal of Information Assurance and Security"},{"key":"e_1_3_1_50_2","article-title":"Different firewall techniques: A survey","author":"Sharma R.K.","year":"2014","unstructured":"SharmaR.K., KalitaH.K. and IssacB., Different firewall techniques: A survey, Computing, Communication and Networking Technologies (ICCCNT), 2014 International Conference on IEEE, 2014.","journal-title":"Computing, Communication and Networking Technologies (ICCCNT), 2014 International Conference on IEEE"},{"issue":"6","key":"e_1_3_1_51_2","article-title":"Generation of biometric key for use in DES","volume":"9","author":"Sharma R.K.","year":"2012","unstructured":"SharmaR.K., Generation of biometric key for use in DES, International Journal of Computer Science Isseues 9(6) (2012).","journal-title":"International Journal of Computer Science Isseues"},{"key":"e_1_3_1_52_2","article-title":"Addressing the curse of imbalanced training sets: One-sided selection","volume":"97","author":"Kubat M.","year":"1997","unstructured":"KubatM. and MatwinS., Addressing the curse of imbalanced training sets: One-sided selection, ICML 97 (1997).","journal-title":"ICML"},{"key":"e_1_3_1_53_2","unstructured":"WittenI.H. et al. Data Mining: Practical machine learning tools and techniques Morgan Kaufmann (2016)."},{"key":"e_1_3_1_54_2","doi-asserted-by":"crossref","unstructured":"DuaS. and XianD. Data mining and machine learning in cybersecurity CRC Press 2016.","DOI":"10.1201\/b10867"},{"key":"e_1_3_1_55_2","unstructured":"HuangR. et al. Learning with a strong adversary arXiv preprint arXiv:1511.03034 2015."},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"issue":"17","key":"e_1_3_1_57_2","first-page":"1","article-title":"Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning","volume":"18","author":"Lema\u00eetre G.","year":"2017","unstructured":"Lema\u00eetreG., NogueiraF. and AridasC.K., Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning, Journal of Machine Learning Research 18(17) (2017), 1\u20135.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-015-0478-7"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-018-3085-1"},{"key":"e_1_3_1_60_2","unstructured":"PapernotN. Adversarial Examples in Machine Learning 2017."},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICWAPR.2017.8076695"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-18202","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-18202","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-18202","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:40:54Z","timestamp":1777455654000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-18202"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,17]]},"references-count":60,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2018,10]]}},"alternative-id":["10.3233\/JIFS-18202"],"URL":"https:\/\/doi.org\/10.3233\/jifs-18202","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,17]]}}}