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A Cooperative Negative Selection Algorithm for Anomaly Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 95 - Number 17 |
| Year of Publication: 2014 |
| Authors: Praneet Saurabh, Bhupendra Verma |
10.5120/16688-6809
|
Praneet Saurabh, Bhupendra Verma . A Cooperative Negative Selection Algorithm for Anomaly Detection. International Journal of Computer Applications. 95, 17 ( June 2014), 27-32. DOI=10.5120/16688-6809
Abstract
Artificial Immune System (AIS) is a convoluted and complex arrangement derived from biological immune system (BIS). It possesses the abilities of self-adapting, self-learning and self-configuration. It has the basic function to distinguish self and non-self. Negative Selection Algorithm (NSA) over the years has shown to be competent for anomaly detection problems. In the past decade internet has popularized and proliferated into our lives immensely. Internet attack cases are increasing with different and new attack methods. This paper presents a Cooperative Negative Selection Algorithm (CNSA) for Anomaly Detection by integrating a novel detector selection strategy and voting between them to effectively identify anomaly. New introduced mechanisms in CNSA enable it to cover more self region correctly and efficiently. It also reduces computational complexities. Experimental results show high anomaly detection rate with less false positive alarm and low overhead in most of the cases.
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