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International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing

作者： Wang, Yujian Luo, Wenjian Univ Sci & Technol China Nat Inspired Computat & Applicat Lab Dept Comp Sci & Technol Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9780769537399

A novel detector generation algorithm for Real-Valued Negative Selection algorithms, i.e. the PTS-RNSA, is proposed in this paper, which is based on the iterative Partition-Test-Spread process. Different from traditional detector generation algorithms that are randomized algorithms, the PTS-RNSA is a deterministic algorithm. When the number of the detectors is large enough, the PTS-RNSA can ensure to cover the whole non-self space except the boundary area between the self space and the non-self space. Experiments are done to compare the PTS-RNSA with the state-of-the-art algorithm, i.e. the V-detector algorithm. Experimental results demonstrate that the performance of the PTS-RNSA is very competitive. Especially, the time cost of the PTS-RNSA is much better than the V-detector algorithm.

关键词： artificial immune system negative selection algorithm detector generation algorithm

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DGA-PSO: An improved detector generation algorithm based on particle swarm optimization in negative selection

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KNOWLEDGE-BASED SYSTEMS 2023年第1期278卷

作者： Zhang, Ge He, Junjiang Li, Wenshan Li, Tao Lan, Xiaolong Wang, Yunpeng Sichuan Univ Sch Cyber Sci & Engn 24 South Sect 1Yihuan Rd Chengdu 610065 Peoples R China Chengdu Univ Informat Technol Sch Cyber Sci & Engn 24 Sect 1Xuefu Rd Southwest Airport Econ Dev Zon Chengdu 610225 Peoples R China

The negative selection algorithm (NSA) is an essential algorithm in the artificial immune system used to achieve anomaly detection by generating detectors. The traditional NSA algorithm generates candidate detectors randomly, which leads to a partially dense and redundant distribution of detectors in the nonself areas, resulting in the presence of holes that are not covered by detectors. A detector generation algorithm based on particle swarm optimization (DGA-PSO) is proposed to overcome these defects. DGA-PSO converts the self-tolerance process into an adaptation function to guide particles to move in a specific direction by artificial settings and variants, generates efficient detectors covering the nonself space, reduces the redundancy among detectors and fills holes not covered. Thus, we successfully reduce the number of detectors while improving the detection rate of the algorithm. Through experimental validation analysis, DGA-PSO ranks first in detector training time and the detection rate on four UCI datasets compared to the classical algorithms RNSA and V-detector and the improved algorithms BIORV-NSA, ADC-NSA and IFB-NSA.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Artificial immune system Negative selection algorithm Particle swarm optimization detector generation algorithm Anomaly detection

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A Study of detector generation algorithms Based on Artificial Immune in Intrusion Detection System

A Study of Detector Generation Algorithms Based on Artificia...

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2011 3rd International Conference on Computer Research and Development(ICCRD 2011)

作者： Chen Jinyin Yang Dongyong Zhejiang University of Technology College of Information Engineering

ISBN: (纸本)9781612848372

detector plays an important role in self and non-self discrimination for intrusion detection system,which makes detector generation a kernel algorithm for artificial immune *** this paper,firstly current used binary matching rules are listed,characteristics of which are *** detector generation algorithm is divided into three main processes,including gene library,negative selection and clone *** for gene library is explained based on the gene library *** new methods are adopted to improve the performance of NSA,and finally cooperative co-evolution detector generation model is constructed which is a novel structure for intrusion detection *** paper is aimed for researchers to focus problems on three main ideas concluded in last chapter.

关键词： artificial immune GA intrusion detection detector generation algorithm negative selection algorithm

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EvoSeedRNSAII: An improved evolutionary algorithm for generating detectors in the real-valued Negative Selection algorithms

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APPLIED SOFT COMPUTING 2014年 19卷 18-30页

作者： Zhang, Jie Luo, Wenjian Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Anhui Peoples R China Univ Sci & Technol China Anhui Key Lab Software Engn Comp & Commun Hefei 230027 Anhui Peoples R China

In the previous work, a detector generation algorithm, named as the EvoSeedRNSA, is proposed. A genetic algorithm is adopted in the EvoSeedRNSA to evolve the random seeds to generate an approximately optimal detector set. This paper proposes an improved EvoSeedRNSA, named as the EvoSeedRNSAII, to generate a more efficient detector set. A multi-group random seed encoding scheme is designed to represent the individuals and different detector generation sequences are discussed. The experiments demonstrate that the EvoSeedRNSAII has a better performance than the EvoSeedRNSA. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Negative Selection algorithm Evolutionary algorithm detector generation algorithm EvoSeedRNSA Encoding schemes

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Generating an Approximately Optimal detector Set by Evolving Random Seeds

Generating an Approximately Optimal Detector Set by Evolving...

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8th IEEE International Conference on Dependable, Autonomic and Secure Computing

作者： Zhang, Jie Luo, Wenjian Xu, Baoliang Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9780769539294

The detector generation algorithm is the core of a Negative Selection algorithm (NSA). In most previous work, the NSAs generate the detector set randomly, which cannot guarantee to obtain an efficient detector set. To generate an approximately optimal detector set, in this paper, a novel detector generation algorithm for the Real-Valued Negative Selection algorithm (RNSA) is proposed. The proposed algorithm, named as the EvoSeedRNSA, adopts a genetic algorithm to evolve the random seeds to obtain an optimized detector set. The experimental results demonstrate that the EvoSeedRNSA has a better performance.

关键词： Negative Selection algorithm Genetic algorithm detector generation algorithm Random Seed

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