{"title":"基于复杂深度神经网络的网络入侵检测系统设计","authors":"M. Al-Shabi","doi":"10.54039/ijcnis.v13i3.5148","DOIUrl":null,"url":null,"abstract":"Recent years have witnessed a tremendous development in various scientific and industrial fields. As a result, different types of networks are widely introduced which are vulnerable to intrusion. In view of the same, numerous studies have been devoted to detecting all types of intrusion and protect the networks from these penetrations. In this paper, a novel network intrusion detection system has been designed to detect cyber-attacks using complex deep neuronal networks. The developed system is trained and tested on the standard dataset KDDCUP99 via pycharm program. Relevant to existing intrusion detection methods with similar deep neuronal networks and traditional machine learning algorithms, the proposed detection system achieves better results in terms of detection accuracy.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"107 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Design of a Network Intrusion Detection System Using Complex Deep Neuronal Networks\",\"authors\":\"M. Al-Shabi\",\"doi\":\"10.54039/ijcnis.v13i3.5148\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent years have witnessed a tremendous development in various scientific and industrial fields. As a result, different types of networks are widely introduced which are vulnerable to intrusion. In view of the same, numerous studies have been devoted to detecting all types of intrusion and protect the networks from these penetrations. In this paper, a novel network intrusion detection system has been designed to detect cyber-attacks using complex deep neuronal networks. The developed system is trained and tested on the standard dataset KDDCUP99 via pycharm program. Relevant to existing intrusion detection methods with similar deep neuronal networks and traditional machine learning algorithms, the proposed detection system achieves better results in terms of detection accuracy.\",\"PeriodicalId\":232613,\"journal\":{\"name\":\"Int. J. Commun. Networks Inf. Secur.\",\"volume\":\"107 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Commun. Networks Inf. Secur.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.54039/ijcnis.v13i3.5148\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Commun. Networks Inf. Secur.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54039/ijcnis.v13i3.5148","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design of a Network Intrusion Detection System Using Complex Deep Neuronal Networks
Recent years have witnessed a tremendous development in various scientific and industrial fields. As a result, different types of networks are widely introduced which are vulnerable to intrusion. In view of the same, numerous studies have been devoted to detecting all types of intrusion and protect the networks from these penetrations. In this paper, a novel network intrusion detection system has been designed to detect cyber-attacks using complex deep neuronal networks. The developed system is trained and tested on the standard dataset KDDCUP99 via pycharm program. Relevant to existing intrusion detection methods with similar deep neuronal networks and traditional machine learning algorithms, the proposed detection system achieves better results in terms of detection accuracy.