NDLI: Proactive defense for evolving cyber threats

Content Provider	IEEE Xplore Digital Library
Author	Colbaugh, R. Glass, K.
Copyright Year	2011
Description	Author affiliation: Sandia National Laboratories, New Mexico Institute of Mining and Technology, Albuquerque, USA (Colbaugh, R.) \|\| New Mexico Institute of Mining and Technology, Socorro, USA (Glass, K.)
Abstract	There is significant interest to develop proactive approaches to cyber defense, in which future attack strategies are anticipated and these insights are incorporated into defense designs. This paper considers the problem of protecting computer networks against intrusions and other attacks, and leverages the coevolutionary relationship between attackers and defenders to derive two new methods for proactive network defense. The first method is a bipartite graph-based machine learning algorithm which enables information concerning previous attacks to be “transferred” for application against novel attacks, thereby substantially increasing the rate with which defense systems can successfully respond to new attacks. The second approach involves exploiting basic threat information (e.g., from cyber security analysts) to generate “synthetic” attack data for use in training defense systems, resulting in networks defenses that are effective against both current and (near) future attacks. The utility of the proposed methods is demonstrated by showing that they outperform standard techniques for the task of detecting malicious network activity in two publicly-available cyber datasets.
Starting Page	125
Ending Page	130
File Size	799545
Page Count	6
File Format	PDF
ISBN	9781457700828
e-ISBN	9781457700859
DOI	10.1109/ISI.2011.5984062
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	cyber security Glass security informatics proactive defense machine learning Niobium predictive analysis
Content Type	Text
Resource Type	Article

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