By Topic

Maintaining Defender's Reputation in Anomaly Detection Against Insider Attacks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Nan Zhang ; Department of Computer Science, George Washington University, Washington, DC, USA ; Wei Yu ; Xinwen Fu ; Sajal K. Das

We address issues related to establishing a defender's reputation in anomaly detection against two types of attackers: 1) smart insiders, who learn from historic attacks and adapt their strategies to avoid detection/punishment, and 2) nai??ve attackers, who blindly launch their attacks without knowledge of the history. In this paper, we propose two novel algorithms for reputation establishment-one for systems solely consisting of smart insiders and the other for systems in which both smart insiders and nai??ve attackers are present. The theoretical analysis and performance evaluation show that our reputation-establishment algorithms can significantly improve the performance of anomaly detection against insider attacks in terms of the tradeoff between detection and false positives.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)  (Volume:40 ,  Issue: 3 )