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Automating Cyberdeception Evaluation with Deep Learning

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Item Summary Ayoade, Gbadebo Araujo, Frederico Al-Naami, Khaled Mustafa, Ahmad Gao, Yang Hamlen, Kevin Khan, Latifur 2020-01-04T07:32:38Z 2020-01-04T07:32:38Z 2020-01-07
dc.identifier.isbn 978-0-9981331-3-3
dc.description.abstract A machine learning-based methodology is proposed and implemented for conducting evaluations of cyberdeceptive defenses with minimal human involvement. This avoids impediments associated with deceptive research on humans, maximizing the efficacy of automated evaluation before human subjects research must be undertaken. Leveraging recent advances in deep learning, the approach synthesizes realistic, interactive, and adaptive traffic for consumption by target web services. A case study applies the approach to evaluate an intrusion detection system equipped with application-layer embedded deceptive responses to attacks. Results demonstrate that synthesizing adaptive web traffic laced with evasive attacks powered by ensemble learning, online adaptive metric learning, and novel class detection to simulate skillful adversaries constitutes a challenging and aggressive test of cyberdeceptive defenses.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Cyber Deception for Defense
dc.subject cyber defense
dc.subject intrusion detection system
dc.subject machine learning
dc.subject penetration testing
dc.subject security
dc.title Automating Cyberdeception Evaluation with Deep Learning
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2020.236
Appears in Collections: Cyber Deception for Defense

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