A random-forests-based classifier using class association rules and its application to an intrusion detection system
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- 作者：Shingo Mabu ; Shun Gotoh ; Masanao Obayashi ; Takashi Kuremoto
- 关键词：Association rule ; Data mining ; Genetic algorithm ; Genetic network programming ; Intrusion detection system ; Random forests
- 刊名：Artificial Life and Robotics
- 出版年：2016
- 出版时间：September 2016
- 年：2016
- 卷：21
- 期：3
- 页码：371-377
- 全文大小：526 KB
- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence and Robotics
Computation by Abstract Devices
Automation and Robotics - 出版者：Springer Japan
- ISSN：1614-7456
- 卷排序：21
文摘
With the rapid developments of network technology, devices connected to the network in a variety of fields have increased, and then, network security has become more important. Rule-based classification for intrusion detection is useful, because it is not only easily understood by humans, but also accurate for the classification of new patterns. Genetic network programming (GNP) is one of the rule-mining techniques as well as the evolutionary-optimization techniques. It can extract rules efficiently even from an enormous database, but still needs more accuracy and stability for practical use. This paper describes a classification system with random forests, employing weighted majority vote in the classification to enhance its performance. For the performance evaluation, NSL-KDD (Network Security Laboratory-Knowledge Discovery and Data Mining) data set is used and the proposed method is compared with the conventional methods, including other machine-learning techniques (Random forests, SVM, J4.8) in terms of the accuracy and false positive rate.