TAILIEUCHUNG - A novel combination of negative and positive selection in Artificial Immune System

In this paper, we propose a novel approach to combining NSAs and PSAs that employ binary representation and r-chunk matching rule. The new algorithm achieves smaller detector storage complexity and potentially better detection time in comparison with single NSAs or PSAs. | VNU Journal of Science: Comp. Science & Com. Eng. Vol. 31, No. 1 (2015) 22–31 A Novel Combination of Negative and Positive Selection in Artificial Immune Systems Van Truong Nguyen1 , Xuan Hoai Nguyen2 , Chi Mai Luong3 1 Thai Nguyen University of Education, Thai Nguyen, Vietnam 2 Hanoi University, Hanoi, Vietnam 3 Vietnamese Academy of Science and Technology, Hanoi, Vietnam Abstract Artificial Immune System (AIS) is a multidisciplinary research area that combines the principles of immunology and computation. Negative Selection Algorithms (NSA) is one of the most popular models of AIS mainly designed for one-class learning problems such as anomaly detection. Positive Selection Algorithms (PSA) is the twin brother of NSA with quite similar performance for AIS. Both NSAs and PSAs comprise of two phases: generating a set D of detectors from a given set S of selves (detector generation phase); and then detecting if a given cell (new data instance) is self or non-self using the generated detector set (detection phase). In this paper, we propose a novel approach to combining NSAs and PSAs that employ binary representation and r-chunk matching rule. The new algorithm achieves smaller detector storage complexity and potentially better detection time in comparison with single NSAs or PSAs. c 2015 Published by VNU Journal of Science. Manuscript communication: received 17 February 2014, revised 01 March 2015, accepted 25 March 2015 Corresponding author: Van Truong Nguyen, nvtruongtn@ Keywords: Artificial immune systems, Negative selection, Positive selection, Intrusion detection, Detector 1. Introduction The biological immune system is a mature defense system which has evolved over millions of years. As a defense system, it is incredibly robust, adaptive, and inherently distributed. The immune system posses powerful pattern recognition, learning, and memory capabilities. It has evolved complex methods for combating infections caused by viruses and other pathogens,

• Kĩ thuật Viễn thông
• Artificial immune systems
• Negative selection
• Positive selection
• Intrusion detection
• r chunk matching rule
• Artificial immune system
• Negative selection algorithm
• Positive selection algorithm
• Combining negative selection
• Opt aiNet
• Construction site layout
• Tabu search
• Tạp chí khoa học
• Improving negative selection algorithm
• Computer virus detection
• Intrusion detection system
• Complete detector repertoire
• Recent novel approaches
• Reduce false detection
• BMC Bioinformatics
• Gillespie algorithm
• Shape space
• Antigenic distance
• Computational immunology
• Humoral immune system