DETECTING WEB-BASED ATTACKS BY MACHINE LEARNING

摘要：

Web-based vulnerabilities represent a substantial portion of the security exposures of computer networks. Unfortunately,many anomaly web-based intrusion detection systems (IDS)take on higher false alarm rate (FAR) and false negative rate (FNR). In this paper, we build this system using Adaboost, a prevailing machine learning algorithm, and its detecting model adopts a dynamic load-balancing algorithm, which can avoid packet loss and false negatives in high-performance web severs with handling heavy traffic loads in real-time and can enhance the efficiency of detecting work. The experiments demonstrate that our system can achieve an especially low false positive rate (approximating 0.3％) and false negative rate (approaching 0.4％) while keeping an extremely low computational complexity.

关键词： Intrusion detection systems (IDS) false alarm rate (FAR) false negative rate (FNR) machine learning

作者: LAI-CHENG CAO

作者单位: School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 2737-2742

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）