A hybrid network intrusion detection system using simplified swarm optimization (SSO)
A hybrid network intrusion detection system using simplified swarm optimization (SSO)
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DOI：
10.1016/j.asoc.2012.04.020
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发表时间：
2012-09-01
影响因子：
8.7
通讯作者：
Wahid, Noorhaniza
中科院分区：
文献类型：
--
作者：
Chung, Yuk Ying;Wahid, Noorhaniza
网络入侵检测技术对于防止系统和网络遭受恶意攻击具有重要意义。然而，传统的网络入侵防御，如防火墙，用户身份验证和数据加密已无法完全保护网络和系统免受日益增长的和复杂的攻击和恶意软件。本文提出了一种新的混合入侵检测系统，采用智能动态群粗糙集（IDS-RS）的特征选择和简化的群优化入侵数据分类。IDS-RS被提出来选择最相关的特征，可以代表网络流量的模式。为了提高单点登录分类器的性能，提出了一种新的加权局部搜索策略。这种新的局部搜索策略的目的是从SSO产生的当前解的邻域中发现更好的解。KDDCup 99数据集上的建议的混合系统的性能进行了评估，通过比较它与标准的粒子群优化（PSO）和其他两个最流行的基准分类。测试结果表明，该混合系统的分类准确率高达93.3%，可以成为入侵检测系统中有竞争力的分类器之一。(C)2012爱思唯尔有限公司版权所有。
The network intrusion detection techniques are important to prevent our systems and networks from malicious behaviors. However, traditional network intrusion prevention such as firewalls, user authentication and data encryption have failed to completely protect networks and systems from the increasing and sophisticated attacks and malwares. In this paper, we propose a new hybrid intrusion detection system by using intelligent dynamic swarm based rough set (IDS-RS) for feature selection and simplified swarm optimization for intrusion data classification. IDS-RS is proposed to select the most relevant features that can represent the pattern of the network traffic. In order to improve the performance of SSO classifier, a new weighted local search (WLS) strategy incorporated in SSO is proposed. The purpose of this new local search strategy is to discover the better solution from the neighborhood of the current solution produced by SSO. The performance of the proposed hybrid system on KDDCup 99 dataset has been evaluated by comparing it with the standard particle swarm optimization (PSO) and two other most popular benchmark classifiers. The testing results showed that the proposed hybrid system can achieve higher classification accuracy than others with 93.3% and it can be one of the competitive classifier for the intrusion detection system. (C) 2012 Elsevier B.V. All rights reserved.