NDLI: Moving target defense for adaptive adversaries

Content Provider	IEEE Xplore Digital Library
Author	Colbaugh, R. Glass, K.
Copyright Year	2013
Description	Author affiliation: Sandia Nat. Labs., Albuquerque, NM, USA (Colbaugh, R.; Glass, K.)
Abstract	Machine learning (ML) plays a central role in the solution of many security problems, for example enabling malicious and innocent activities to be rapidly and accurately distinguished and appropriate actions to be taken. Unfortunately, a standard assumption in ML - that the training and test data are identically distributed - is typically violated in security applications, leading to degraded algorithm performance and reduced security. Previous research has attempted to address this challenge by developing ML algorithms which are either robust to differences between training and test data or are able to predict and account for these differences. This paper adopts a different approach, developing a class of moving target (MT) defenses that are difficult for adversaries to reverse-engineer, which in turn decreases the adversaries' ability to generate training/test data differences that benefit them. We leverage the coevolutionary relationship between attackers and defenders to derive a simple, flexible MT defense strategy which is optimal or nearly optimal for a broad range of security problems. Case studies involving two distinct cyber defense applications demonstrate that the proposed MT algorithm outperforms standard static methods, offering effective defense against intelligent, adaptive adversaries.
Sponsorship	IEEE Intelligent Transp. Syst. Soc.
Starting Page	50
Ending Page	55
File Size	560257
Page Count	6
File Format	PDF
ISBN	9781467362146
e-ISBN	9781467362139
DOI	10.1109/ISI.2013.6578785
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training moving target defense adaptive adversaries Biological system modeling cyber security Unsolicited electronic mail Switches Games hybrid dynamical systems Security machine learning
Content Type	Text
Resource Type	Article

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