HoneyCode: Automating Deceptive Software Repositories with Deep Generative Models

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dc.contributor.authorNguyen, David
dc.contributor.authorLiebowitz, David
dc.contributor.authorNepal, Surya
dc.contributor.authorKanhere, Salil
dc.date.accessioned2020-12-24T20:26:55Z
dc.date.available2020-12-24T20:26:55Z
dc.date.issued2021-01-05
dc.description.abstractWe propose HoneyCode, an architecture for the generation of synthetic software repositories for cyber deception. The synthetic repositories have the characteristics of real software, including language features, file names and extensions, but contain no real intellectual property. The fake software can be used as a honeypot or form part of a deceptive environment. Existing approaches to software repository generation lack scalability due to reliance on hand-crafted structures for specific languages. Our approach is language agnostic and learns the underlying representations of repository structures, filenames and file content through a novel Tree Recurrent Network (TRN) and two recurrent networks (RNN) respectively. Each stage of the sequential generation process utilises features from prior steps, which increases the honey repository’s authenticity and consistency. Experiments show TRN generates tree samples that reduce degree mean maximal distance (MMD) by 90-92% and depth MMD by 75-86% to a held out test data set in comparison to recent deep graph generators and a baseline random tree generator. In addition, our RNN models generate convincing filenames with authentic syntax and realistic file content.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2021.835
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/71456
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCyber Operations, Defence, and Forensics
dc.subjectcyber defence
dc.subjectdeception
dc.subjectgenerative models
dc.subjecthoneypot
dc.subjectneural networks
dc.titleHoneyCode: Automating Deceptive Software Repositories with Deep Generative Models
prism.startingpage6945

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