A NOVEL ACTIVE COST-SENSITIVE LEARNING METHOD FOR INTRUSION DETECTION

摘要：

Intrusion detection systems (IDS) protect computer systems by providing alerts which might be caused by malicious attacks. Learning methods were introduced into intrusion detection to automatically improve the performance using history data. Yet high quality data requires heavy labor of experts or expensive monitoring process. Meanwhile, IDS should minimize a nonuniform cost of the misclassification. In the paper, we aim to reduce the burden of labeling data for constructing the intrusion detection classifier with the least misclassification cost. We proposed a novel active cost-sensitive learning method for intrusion detection using the technologies of active learning and cost-sensitive learning. The proposed method uses a popular cost-sensitive learning method Metacost as the base classifier and a sampling criterion of the largest misclassification cost. The results of the experiments on intrusion detection datasets of KDDCUP 99 show that the proposed method is effective.

关键词： Intrusion detection Active Learning Cost-sensitive learning

作者: JUN LONG JIAN-PING YIN EN ZHU WEN-TAO ZHAO

作者单位: School of Computer Science, National University of Defense technology, Changsha 410073, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1099-1104

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）