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Network intrusion detection through artificial immune system

机译：通过人工免疫系统进行网络入侵检测

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摘要

Intrusion Detection Systems (IDS) are security technologies. In this regard, Artificial Immune System (AIS) which provides distributed detection through its lymphocytes is an appealing approach for designing IDSs. In this paper, an AIS based intrusion detection is proposed in which two sets of antibodies - positive and negative - are generated for normal and attack samples respectively using negative selection and positive selection theories in primary detectors' generation. Standard Particle Swarm Optimization (PSO) is employed for training immature detectors to improve detection rate. Moreover, antibodies' radiuses is dynamically determined through generation and training algorithms. Simulation shows that the proposed algorithm achieved 99.1% true positive rate while the false positive rate is 1.9%.
机译：入侵检测系统（IDS）是安全技术。在这方面，通过其淋巴细胞提供分布式检测的人工免疫系统（AIS）是设计IDS的诱人方法。在本文中，提出了一种基于AIS的入侵检测方法，其中在主要检测器的生成中分别使用阴性选择和阳性选择理论分别为正常样品和攻击样品生成了两组抗体（阳性和阴性）。标准粒子群优化（PSO）用于训练未成熟的检测器，以提高检测率。此外，抗体的半径是通过生成和训练算法动态确定的。仿真结果表明，所提算法的正确率为99.1％，错误率为1.9％。

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