Applying Artificial Immune System for Intrusion Detection

被引：12

作者：

Hooks, Daniel ^{[1
]}

Yuan, Xiaohong ^{[2
]}

Roy, Kaushik ^{[2
]}

Esterline, Albert ^{[2
]}

Hernandez, Joaquin ^{[2
]}

机构：

[1] Univ North Carolina Greensboro, Dept Comp Sci, Greensboro, NC 27412 USA

[2] North Carolina Agr & Tech State Univ, Dept Comp Sci, Greensboro, NC USA

来源：

2018 IEEE FOURTH INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING SERVICE AND APPLICATIONS (IEEE BIGDATASERVICE 2018) | 2018年

关键词：

Human Immune System (HIS); Artificial Immune Systems (AIS); Intrusion Detection Systems (IDS); Negative Selection Algorithm (NSA); Clonal Selection Algorithm (CSA);

D O I：

10.1109/BigDataService.2018.00051

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper investigates the approaches of using an analogy of the Human Immune System (HIS) to create an Artificial Immune System (AIS) based Intrusion Detection System (IDS). The two most popular AIS generating algorithms, Negative and Clonal Selection were explored and tested on the NSL-KDD dataset with different sets of features and different numbers of detectors. The experiments show that the Negative Selection Algorithm (NSA) and the Clonal Selection Algorithm (CSA) show a severe scaling issue when handling real network traffic.

引用

页码：287 / 292

页数：6

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