Applying fuzzy neural network to intrusion detection based on sequences of system calls

Authors:
Guiling Zhang;Jizhou Sun
Affiliations:
Department of Electronic Information Engineering, Tianjin University;Department of Electronic Information Engineering, Tianjin University
Venue:
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Year:
2005

Citing 5
Cited 0

Data mining approaches for intrusion detection

SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Learning program behavior profiles for intrusion detection

ID'99 Proceedings of the 1st conference on Workshop on Intrusion Detection and Network Monitoring - Volume 1
Intrusion detection using sequences of system calls

Journal of Computer Security
A sense of self for Unix processes

SP'96 Proceedings of the 1996 IEEE conference on Security and privacy
Intrusion detection techniques and approaches

Computer Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Short sequences of system calls have been proven to be a good signature description for anomalous intrusion detection. The signature provides clear separation between different kinds of programs. This paper extends these works by applying fuzzy neural network (FNN) to solve the sharp boundary problem and decide whether a sequence is “normal” or “abnormal”. By using threat level of system calls to label the sequences the proposed FNN improves the accuracy of anomaly detection.