{"title":"Implementation of Network Intrusion Detection System Using Soft Computing Algorithms (Self Organizing Feature Map and Genetic Algorithm)","authors":"J. T. Hounsou, Thierry Nsabimana, Jules Degila","doi":"10.4236/JIS.2019.101001","DOIUrl":null,"url":null,"abstract":"In today’s world, computer network is evolving very rapidly. Most public or/and private companies set up their own local networks system for the purpose of promoting communication and data sharing within the companies. Unfortunately, their data and local networks system are under risks. With the advanced computer networks, the unauthorized users attempt to access their local networks system so as to compromise the integrity, confidentiality and availability of resources. Multiple methods and approaches have to be applied to protect their data and local networks system against malicious attacks. The main aim of our paper is to provide an intrusion detection system based on soft computing algorithms such as Self Organizing Feature Map Artificial Neural Network and Genetic Algorithm to network intrusion detection system. KDD Cup 99 and 1998 DARPA dataset were employed for training and testing the intrusion detection rules. However, GA’s traditional Fitness Function was improved in order to evaluate the efficiency and effectiveness of the algorithm in classifying network attacks from KDD Cup 99 and 1998 DARPA dataset. SOFM ANN and GA training parameters were discussed and implemented for performance evaluation. The experimental results demonstrated that SOFM ANN achieved better performance than GA, where in SOFM ANN high attack detection rate is 99.98%, 99.89%, 100%, 100%, 100% and low false positive rate is 0.01%, 0.1%, 0%, 0%, 0% for DoS, R2L, Probe, U2R attacks, and Normal traffic respectively.","PeriodicalId":57259,"journal":{"name":"信息安全(英文)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"信息安全(英文)","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.4236/JIS.2019.101001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In today’s world, computer network is evolving very rapidly. Most public or/and private companies set up their own local networks system for the purpose of promoting communication and data sharing within the companies. Unfortunately, their data and local networks system are under risks. With the advanced computer networks, the unauthorized users attempt to access their local networks system so as to compromise the integrity, confidentiality and availability of resources. Multiple methods and approaches have to be applied to protect their data and local networks system against malicious attacks. The main aim of our paper is to provide an intrusion detection system based on soft computing algorithms such as Self Organizing Feature Map Artificial Neural Network and Genetic Algorithm to network intrusion detection system. KDD Cup 99 and 1998 DARPA dataset were employed for training and testing the intrusion detection rules. However, GA’s traditional Fitness Function was improved in order to evaluate the efficiency and effectiveness of the algorithm in classifying network attacks from KDD Cup 99 and 1998 DARPA dataset. SOFM ANN and GA training parameters were discussed and implemented for performance evaluation. The experimental results demonstrated that SOFM ANN achieved better performance than GA, where in SOFM ANN high attack detection rate is 99.98%, 99.89%, 100%, 100%, 100% and low false positive rate is 0.01%, 0.1%, 0%, 0%, 0% for DoS, R2L, Probe, U2R attacks, and Normal traffic respectively.
当今世界,计算机网络发展非常迅速。大多数公营或/及私营公司都设立了自己的本地网络系统,以促进公司内部的通讯和数据共享。不幸的是,他们的数据和本地网络系统处于危险之中。随着先进的计算机网络,未经授权的用户试图访问本地网络系统,从而损害资源的完整性、保密性和可用性。必须采用多种方法和途径来保护他们的数据和本地网络系统免受恶意攻击。本文的主要目的是为网络入侵检测系统提供一种基于自组织特征映射、人工神经网络和遗传算法等软计算算法的入侵检测系统。采用KDD Cup 99和1998年DARPA数据集训练和测试入侵检测规则。然而,为了评估算法对KDD Cup 99和1998 DARPA数据集的网络攻击分类的效率和有效性,对遗传算法的传统适应度函数进行了改进。讨论并实现了SOFM、ANN和GA训练参数的性能评价。实验结果表明,SOFM神经网络的性能优于遗传算法,SOFM神经网络对DoS攻击、R2L攻击、Probe攻击、U2R攻击和Normal流量的高检测率分别为99.98%、99.89%、100%、100%、100%,低误报率分别为0.01%、0.1%、0%、0%、0%。