Anomaly Detection Using a Novel Negative Selection Algorithm

作者：Zeng, Jinquan^*; Qin, Zhiguang; Tang, Weiwen

来源：Journal of Computational and Theoretical Nanoscience, 2013, 10(12): 2831-2835.

DOI：10.1166/jctn.2013.3286

• Artificial Immune Systems
• Negative Selection Algorithm
• Anomaly Detection

Negative selection algorithm (NSA) is one of the major algorithms developed within artificial immune system (AIS) and can be used for network security, fault detection, especially, anomaly detection. NSA generates the detectors based on the self space. Due to the drawbacks of the current representation of the self space in NSAs, the generated detectors cannot enough cover the non-self space and at the same time, cover some of the self space. In this paper, an extension of real-valued negative selection algorithm with the variable-sized self radius and bi-directional matching rule are introduced. Using the variable-sized self radius and bi-directional matching rule, we can construct an appropriate profile of the system, and then based on the constructed profile of the system we produce the more quality detectors to cover the non-self space; at the same time, using the variable-sized self radius and bi-directional matching rule, we can decrease the number of detectors and cover enough the non-self space. In our experiments, this approach is tested using the well-known real world datasets; preliminary results show that the new approach improves the overall performance of detectors and without increase in complexity.

• 出版日期2013-12
• 单位电子科技大学