In this paper, we propose an improvement of r-chunk type detector-based NSA by combining negative selection and positive selection to reduce runtime complexity and memory complexity. | Nguyễn Văn Trường và Đtg Tạp chí KHOA HỌC & CÔNG NGHỆ 106(06): 41 - 47 COMBINING NEGATIVE SELECTION AND POSITIVE SELECTION IN ARTIFICIAL IMMUNE SYSTEMS Nguyen Van Truong1*, Vu Thi Nguyet Thu1, Trinh Van Ha2 2 1 College of Education – TNU College of Information and Communication Technology - TNU SUMMARY Artificial Immune System (AIS) is a diverse research area that combines the disciplines of immunology and computation. Negative Selection Algorithm (NSA) and Positive selection algorithm (PSA) are two famous models of AIS designed for anomaly detection. They all contain two stages: generating a set D of detectors from a given set S of self; detecting if a given cell is self or non-self using generated detectors. In this paper, we propose an improvement of r-chunk type detector-based NSA by combining negative selection and positive selection to reduce runtime complexity and memory complexity. Key words: Artificial immune system, negative selection algorithm, positive selection algorithm, computer security, r-chunk detector. INTRODUCTION* The biological immune system is able to recognize which cells are its own (self) and which are foreign (non-self, such as bacteria or viruses). The representative immune cell is the T cell, which has a self-recognition component and an antigen receptor for locating and eliminating infected cells. By modeling the characteristics of the biological immune system, the system that protects from damage by external attacks and eliminates intruders in the case of computer perspectives is called the artificial immune system [3]. Biological immune system is a complex, self organizing and highly distributed system. It has no centralized control and uses learning and memorizing when solving particular tasks [11]. The learning process does not require negative examples and acquired knowledge is represented in an explicit form: T cells are generated randomly and in a large number, in the hope that every pathogen that infects the host is .
