Optimization of Real-Valued Self Set for Anomaly Detection Using Gaussian Distribution

Authors:
Liang Xi;Fengbin Zhang;Dawei Wang
Affiliations:
College of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China 150080;College of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China 150080;College of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China 150080
Venue:
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Year:
2009

Citing 6
Cited 0

Self-Nonself Discrimination in a Computer

SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Architecture for an Artificial Immune System

Evolutionary Computation
An overview of anomaly detection techniques: Existing solutions and latest technological trends

Computer Networks: The International Journal of Computer and Telecommunications Networking
An agent based and biological inspired real-time intrusion detection and security model for computer network operations

Computer Communications
A hybrid artificial immune system and Self Organising Map for network intrusion detection

Information Sciences: an International Journal
An immunity-based technique to characterize intrusions in computernetworks

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

The real-valued negative selection algorithm (RNS) has been a key algorithm of anomaly detection. However, the self set which is used to train detectors has some problems, such as the wrong samples, boundary invasion and the overlapping among the self samples. Due to the fact that the probability of most real-valued self vectors is near to Gaussian distribution, this paper proposes a new method which uses Gaussian distribution theory to optimize the self set before training stage. The method was tested by 2-dimensional synthetic data and real network data. Experimental results show that, the new method effectively solves the problems mentioned before.