By Topic

Network intrusion detection with Fuzzy Genetic Algorithm for unknown 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
$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)
Jongsuebsuk, P. ; Dept. of Comput. Eng., King Mongkut's Univ. of Technol., Bangkok, Thailand ; Wattanapongsakorn, N. ; Charnsripinyo, C.

In this work, we consider detecting unknown or new network attack types with a Fuzzy Genetic Algorithm approach. The fuzzy rule is a supervised learning technique and genetic algorithm make fuzzy rule able to learn new attacks by itself. Moreover, this technique has high detection rate and robust. Therefore, we apply the fuzzy genetic algorithm approach to our real-time intrusion detection system implementation i.e. the data is detected right after it arrived to the detection system. In our experiments, various denial of service (DoS) attacks and Probe attacks are considered. We evaluate our IDS in terms of detection time, detection rate and false alarm rate. From the experiment, we obtain the average detection rate approximately over 97%.

Published in:

Information Networking (ICOIN), 2013 International Conference on

Date of Conference:

28-30 Jan. 2013