By Topic

Utilizing statistical characteristics of N-grams for intrusion detection

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)
Li Zhuowei ; Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore ; Das, A. ; Nandi, S.

Information and infrastructure security is a serious issue of global concern. As the last line of defense for security infrastructure, intrusion detection techniques are paid more and more attention. In this paper, one anomaly-based intrusion detection technique (ScanAID: Statistical ChAracteristics of N-grams for Anomaly-based Intrusion Detection) is proposed to detect intrusive behaviors in a computer system. The statistical properties in sequences of system calls are abstracted to model the normal behaviors of a privileged process, in which the model is characterized by a vector of anomaly values of N-grams. With a reasonable definition of efficiency parameter, the length of an N-gram and the size of the training dataset are optimized to get an efficient and compact model. Then, with the optimal modeling parameters, the flexibility and efficiency of the model are evaluated by the ROC curves. Our experimental results show that the proposed statistical anomaly detection technique is promising and deserves further research (such as applying it to network environments).

Published in:

Cyberworlds, 2003. Proceedings. 2003 International Conference on

Date of Conference:

3-5 Dec. 2003