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Automating Cyberdeception Evaluation with Deep Learning

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Title:Automating Cyberdeception Evaluation with Deep Learning
Authors:Ayoade, Gbadebo
Araujo, Frederico
Al-Naami, Khaled
Mustafa, Ahmad
Gao, Yang
show 2 moreHamlen, Kevin
Khan, Latifur
show less
Keywords:Cyber Deception for Defense
cyber defense
intrusion detection system
machine learning
penetration testing
show 1 moresecurity
show less
Date Issued:07 Jan 2020
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63975
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.236
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Cyber Deception for Defense


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