Adversarial Anomaly Detection Using Centroid-Based Clustering

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dc.contributor.authorAnindya, I. C.
dc.contributor.authorKantarcioglu, Murat
dc.contributor.utdAuthorKantarcioglu, Murat
dc.date.accessioned2019-06-28T18:55:40Z
dc.date.available2019-06-28T18:55:40Z
dc.date.created2018-07-07
dc.descriptionFull text access from Treasures at UT Dallas is restricted to current UTD affiliates.
dc.description.abstractAs cyber attacks are growing with an unprecedented rate in the recent years, organizations are seeking an efficient and scalable solution towards a holistic protection system. As the adversaries are becoming more skilled and organized, traditional rule based detection systems have been proved to be quite ineffective against the continuously evolving cyber attacks. Consequently, security researchers are focusing on applying machine learning techniques and big data analytics to defend against cyber attacks. Over the recent years, several anomaly detection systems have been claimed to be quite successful against the sophisticated cyber attacks including the previously unseen zero-day attacks. But often, these systems do not consider the adversary's adaptive attacking behavior for bypassing the detection procedure. As a result, deploying these systems in active real-world scenarios fails to provide significant benefits in the presence of intelligent adversaries that are carefully manipulating the attack vectors. In this work, we analyze the adversarial impact on anomaly detection models that are built upon centroid-based clustering from game-theoretic aspect and propose adversarial anomaly detection technique for these models. The experimental results show that our game-theoretic anomaly detection models can withstand attacks more effectively compared to the traditional models.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.description.sponsorshipNIH award 1R01HG006844, NSF awards CNS-1111529, CICI-1547324, and IIS-1633331 and ARO award W911NF-17-1-0356.
dc.identifier.bibliographicCitationAnindya, I. C., and M. Kantarcioglu. 2018. "Adversarial anomaly detection using centroid-based clustering." Proceedings - 2018 IEEE 19th International Conference on Information Reuse and Integration for Data Science, IRI 2018: 1-8, doi:10.1109/IRI.2018.00009
dc.identifier.isbn9781538626597
dc.identifier.urihttps://hdl.handle.net/10735.1/6639
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.isPartOf2018 IEEE 19th International Conference on Information Reuse and Integration for Data Science (IRI). 7-9 July 2018, Salt Lake City, UT
dc.relation.urihttp://dx.doi.org/10.1109/IRI.2018.00009
dc.rights©2018 IEEE
dc.subjectMachine learning
dc.subjectAnomaly detection (Computer security)
dc.subjectCluster analysis--Data processing
dc.subjectArtificial intelligence
dc.subjectBig data
dc.subjectComputer crimes
dc.subjectData integration (Computer science)
dc.subjectGame theory
dc.subjectComputer networks--Security measures
dc.subjectAnomaly detection (Computer security)
dc.subjectComputer crime
dc.titleAdversarial Anomaly Detection Using Centroid-Based Clustering
dc.type.genrearticle

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