By Topic

Network Intrusion Detection Using CFAR Abrupt-Change Detectors

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

2 Author(s)
Di He ; Shanghai Jiao Tong Univ., Shanghai ; Leung, H.

In this paper, the constant false alarm rate (CFAR) detectors are proposed for network intrusion detection. By using an autoregressive system to model the network traffic, predictor error is shown to closely follow a Gaussian distribution. CFAR detector approaches are then developed on the prediction error distribution. In the present study, we consider the optimal CFAR, the cell-averaging CFAR, and the order statistics CFAR. The use of these CFAR techniques can significantly improve the detection performance. In addition, we propose the use of fusion of these CFAR detectors by using Dempster-Shafer and Bayesian techniques. Computer simulations based on the DARPA traffic data show that the proposed approach achieves higher detection probabilities than the conventional detection method. Even under different types of attacks, the intrusion detection performances based on the proposed CFAR detectors shows consistent improvement.

Published in:

Instrumentation and Measurement, IEEE Transactions on  (Volume:57 ,  Issue: 3 )