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An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications

Computers, Materials & Continua 2022年第8期72卷 3529-3548页

作者： Ghalib H.Alshammri Amani K.Samha Ezz El-Din Hemdan Mohammed Amoon Walid El-Shafai Department of Computer Science Community CollegeKing Saud UniversityRiyadh28095Saudi Arabia Deanship of Scientific Research Saudi Electronic UniversityRiyadhSaudi Arabia Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh28095Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Electronics and Electrical Communications Eng.Depart. Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process.In recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable controller.Therefore,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN environment.Consequently,this paper provides an extensive analysis and investigation of the NSL-kdd dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five algorithms.Furthermore,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the kdd(Knowledge Discovery in Databases)dataset.First,the utilized dataset is clustered into normal and four major attack categories via the clustering process.Then,a deep learning method is projected for building an efficient SDN-based intrusion detection system.The results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the kdd dataset.Similarly,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing techniques.For example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.

关键词： Deep neural network DL WEKA network traffic intrusion and anomaly detection SDN clustering and classification kdd dataset

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Intrusion Detection System Using Decision Tree Algorithm

Intrusion Detection System Using Decision Tree Algorithm

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2012 IEEE 14th International Conference on Communication Technology

作者： Manish Kumar M.Hanumanthappa T.V.Suresh Kumar Dept.of Master of Computer Applications M.S.Ramaiah Institute of Technology Research Scholar Department of Computers Science and ApplicationsBangalore University Dept.of Computer Science and Applications Jnana Bharathi CampusBangalore University

Intrusion Detection System （IDS） is the most powerful system that can handle the intrusions of the computer environments by triggering alerts to make the analysts take actions to stop this intrusion.IDS’s are based on the belief that an intruder’s behavior will be noticeably different from that of a legitimate user.A variety of intrusion detection systems （IDS） have been employed for protecting computers and networks from malicious attacks by using traditional statistical methods to new data mining approaches in last decades.However,today’s commercially available intrusion detection systems are signature- based that are not capable of detecting unknown attacks.In this paper we analyze a classification model for misuse and anomaly attack detection using decision tree algorithm.

关键词： Intrusion Detection System kdd dataset Network Security Decision Tree Algorithm

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