Dynamically real-time intrusion detection algorithm with immune network

被引：0

作者：

Hu, Xinlei ^{[1
]}

Liu, Xiaojie ^{[1
]}

Li, Tao ^{[1
]}

Yang, Tao ^{[2
]}

Chen, Wen ^{[1
]}

Liu, Zhengjun ^{[1
]}

机构：

[1] College of Computer Science, Sichuan University, Chengdu

[2] China West Normal University, Nanchong

来源：

Journal of Computational Information Systems | 2015年 / 11卷 / 02期

关键词：

Artificial immune; Immune network; Intrusion detection; Stimulation level;

D O I：

10.12733/jcis13038

中图分类号：

学科分类号：

摘要：

The deficiency of traditional immune-based intrusion model is that the definition of detector will not change after it has been defined, the static definition of detection profiles could not well adapt to the real complex network environment. This results in lower detection rate and higher false alarm rate. Dynamically real-time intrusion detection algorithm with immune network (DIDAIN) is proposed in this paper. We establish a quantitative description for the model, and adopt the immune network stimulation-suppression mechanism to real-time update mature detector set dynamically. The dynamic definition of detector could well adapt to the real complex network environment. Meanwhile, the stimulation of a mature detector in the immune network decides the probability of clone and mutation. The higher the stimulation level is, the greater the probability of clone and mutation makes excellent detector retained. Under KDD Cup99 dataset, comparing with the traditional immune-based intrusion models, the proposed algorithm DIDAIN could get the highest detection rate and the lowest false alarm rate. 1553-9105/Copyright © 2015 Binary Information Press

引用

页码：587 / 594

页数：7

共 20 条

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