TAILIEUCHUNG - A new approach for generating a complete detector repertoire in artificial immune systems

We present a novel data structure for extracting data from protected self set. That helps to produce all possible detectors, or a complete detector repertoire, with lower time and space complexities compare to recent novel approaches. Theoretical analysis and experimental results show that our approach is effective and feasible. These new valuable characteristics can make complex AIS have the highest ability to detect data changes and to reduce false detection. | Nguyễn Văn Trường và đtg Tạp chí KHOA HỌC & CÔNG NGHỆ 80(04): 121 - 125 A NEW APPROACH FOR GENERATING A COMPLETE DETECTOR REPERTOIRE IN ARTIFICIAL IMMUNE SYSTEMS Nguyen Van Truong1, Pham Dinh Lam2 1 College of Education – TNU; 2 Board of Information Technology - TNU ABSTRACT Generation of the Detector set is a key problem in Artificial Immune Systems (AIS) due to cost of time and space. A weakness in many detector generating algorithms was random generation of detectors so that many candidate detectors may be discarded. We present a novel data structure for extracting data from protected self set. That helps to produce all possible detectors, or a complete detector repertoire, with lower time and space complexities compare to recent novel approaches. Theoretical analysis and experimental results show that our approach is effective and feasible. These new valuable characteristics can make complex AIS have the highest ability to detect data changes and to reduce false detection. Keywords: Artificial immune system, Intrusion detection system, self set, detector set, complete detector repertoire, false detection INTRODUCTION* It is impractical for security software to detect all intrusions in computer networks. Many network technologies on security came into being. The more successful one is based on AIS, which illustrates biology immune system on computers. It is evaluated as a new and effective soft computing method in the field of network security and information security. Especially, it is suitable for building up the Intrusion Detection Systems (IDS) that can protect computer systems against intruders and the destruction of computer viruses or other malicious software system. This paper is concerned with one aspect of computer security: ability of detecting all kinds of changes or attacks, both known and unknown ones. The method explored involves the most discussed immune models: Negative selection algorithm. In the algorithm, generating the detector set .

• Giáo dục học
• Artificial immune system
• Intrusion detection system
• Complete detector repertoire
• Recent novel approaches
• Reduce false detection
• Uncertainty modelling
• Fuzzy sets
• Immune system approach
• Transportation phenomenon characterized
• Negative selection algorithm
• Positive selection algorithm
• Combining negative selection
• Artificial immune systems
• Opt aiNet
• Construction site layout
• Tabu search
• Master's thesis of Engineering
• Master's thesis
• KDD Cup 99
• Negative selection
• Positive selection
• Intrusion detection
• r chunk matching rule
• Tạp chí khoa học
• Improving negative selection algorithm
• Computer virus detection
• BMC Bioinformatics
• Gillespie algorithm
• Shape space
• Antigenic distance
• Computational immunology
• Humoral immune system
• Software fault prediction
• Machine learning
• Random forest
• Software system
• Email spam filtering using R chunk detector
• R chunk detector
• Another look at R chunk detector
• Given self set S
• The performance of NSA
• The overall runtime complexity significantly