Coverage and Generalization in an Artificial Immune System
Coverage and Generalization in an Artificial Immune System
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发表时间：
2002-07
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通讯作者：
Justin Balthrop;Fernando Esponda;Stephanie Forrest;Matthew R. Glickman
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作者：
Justin Balthrop;Fernando Esponda;Stephanie Forrest;Matthew R. Glickman
LISYS是一个专门针对网络入侵检测问题的人工免疫系统框架。LISYS通过观察正常的网络流量来检测异常数据包。因为LISYS只看到正常流量的部分样本，所以它必须从其观察结果中进行归纳，以便正确地描述正常行为。介绍了r-连续位匹配规则的一种变体，并研究了其对覆盖率和泛化的影响。还探讨了表示多样性的覆盖率和泛化的影响，通过研究置换的位的顺序表示。
LISYS is an artificial immune system framework which is specialized for the problem of network intrusion detection. LISYS learns to detect abnormal packets by observing normal network traffic. Because LISYS sees only a partial sample of normal traffic, it must generalize from its observations in order to characterize normal behavior correctly. A variation of the r-contiguous bits matching rule is introduced, and its effect on coverage and generalization is studied. The effect of representation diversity on coverage and generalization is also explored by studying permutations in the order of bits in the representation.