Cart (Loading....) | Create Account
Close category search window
 

An Empirical Approach to Modeling Uncertainty in Intrusion Analysis

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
$31 $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

3 Author(s)
Xinming Ou ; Kansas State Univ., Manhattan, KS, USA ; Rajagopalan, S.R. ; Sakthivelmurugan, S.

Uncertainty is an innate feature of intrusion analysis due to the limited views provided by system monitoring tools, intrusion detection systems (IDS), and various types of logs. Attackers are essentially invisible in cyber space and monitoring tools can only observe the symptoms or effects of malicious activities. When mingled with similar effects from normal or non-malicious activities they lead intrusion analysis to conclusions of varying confidence and high false positive/negative rates. This paper presents an empirical approach to the problem of uncertainty where the inferred security implications of low-level observations are captured in a simple logical language augmented with certainty tags. We have designed an automated reasoning process that enables us to combine multiple sources of system monitoring data and extract highly-confident attack traces from the numerous possible interpretations of low-level observations. We have developed our model empirically: the starting point was a true intrusion that happened on a campus network that we studied to capture the essence of the human reasoning process that led to conclusions about the attack. We then used a Datalog-like language to encode the model and a Prolog system to carry out the reasoning process. Our model and reasoning system reached the same conclusions as the human administrator on the question of which machines were certainly compromised. We then automatically generated the reasoning model needed for handling Snort alerts from the natural-language descriptions in the Snort rule repository, and developed a Snort add-on to analyze Snort alerts. Keeping the reasoning model unchanged, we applied our reasoning system to two third-party data sets and one production network. Our results showed that the reasoning model is effective on these data sets as well. We believe such an empirical approach has the potential of codifying the seemingly ad-hoc human reasoning of uncertain events, and can yield useful to- ols for automated intrusion analysis.

Published in:

Computer Security Applications Conference, 2009. ACSAC '09. Annual

Date of Conference:

7-11 Dec. 2009

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.