A statistical Framework for Intrusion Detection System

摘要：

　　This paper proposes a statistical framework for intrusion detection system based on sampling with Least Square Support Vector Machine(LS-SVM).Decision making is performed in two stages.In the first stage,the whole dataset is divided into some predetermined arbitrary subgroups.The proposed algorithm selects representative samples from these subgroups such that the samples reflect the entire dataset.An optimum allocation scheme is developed based on the variability of the observations within the subgroups.In the second stage,least square support vector machine(LS-SVM)is applied to the extracted samples to detect intrusions.We call the proposed algorithm as optimum allocation-based least square support vector machine(OA-LS-SVM)for IDS.To demonstrate the effectiveness of the proposed method,the experiments are carried out on KDD 99 database which is considered a de facto benchmark for evaluating the performance of intrusions detection algorithm.All binary-classes are tested and our proposed approach obtains a realistic performance in terms of accuracy and efficiency.

作者: Md Enamul Kabir Jiankun Hu

作者单位: School of Human Movement Studies University of Queensland St Lucia, QLD 4072, Australia School of Engineering and Information Technology University of New South Wales at the Australian Def

会议类型: 国际会议

会议名称: The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

会议地点: 厦门

会议语种:英文

页码: 953-958

在线出版日期: 2014-08-19（万方平台首次上网日期，不代表论文的发表时间）