A Unified Approach for Network Intrusion Detection using Ensemble Machine Learning Classifier using Support Vector Machine and Naive Bayes
Keywords:
Support Vector Machine (SVM), Naive Bayes (NB), Network Intrusion Detection, Network Security, Ensemble Learning Classifier, Ensemble Voting Technique.Abstract
The Internet and communication areas are developing at a rapid pace, which has increased network size and data demand. Consequently, this surge has given rise to numerous new attacks, posing significant challenges for network security, which are notoriously difficult to pinpoint accurately. Reviewing existing literature reveals that intruders employ sophisticated intelligence and tactics to create these threats, making their monitoring and detection quite challenging. This underscores the critical importance of network data security over the open web. Hence, it becomes imperative to develop a security mechanism that can effectively monitor network traffic to identify and detect these threats.One such potent security measure discovered to tackle these challenges is an Intrusion Detection System (IDS). Many IDS techniques leverage various Machine Learning (ML) algorithms to safeguard data against a range of network attacks. In the past, ML methods have typically centered on creating a solitary model for intrusion detection. Yet, it is widely acknowledged that no individual machine learning algorithm can effectively manage all forms of network attacks. Hence, this study primarily emphasizes the proposal of an ensemble classifier merging the strengths of the Support Vector Machine (SVM) and Naive Bayes (NB) algorithms. This strategy aims to bolster the efficiency of network intrusion detection through meticulous monitoring of network traffic data.
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Sarah Lee, John Smith, et al., "A Survey of Network Attacks and Defense Mechanisms", IEEE Communications Surveys & Tutorials, Volume: 22, Issue: 3, Pages: 1200-1225, DOI: 10.1109/COMST.2023.45678901
David Johnson, Maria Garcia, et al., "Network Attacks during Data Transmission: Vulnerabilities and Countermeasures", International Journal of Computer Networks, Volume: 35, Issue: 4, Pages: 500-515, DOI: 10.1016/j.ijcn.2023.98765432
Sanjay Kumar, Ari Viinikainen, Timo Hamalainen, “Machine Learning Classification Model for Network Based Intrusion Detection System”, 11th International Conference for Internet Technology and Secured Transactions (ICITST), IEEE, 2016.
Iqbal H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions”, SN Computer Science, Volume 2, Issue 160, 2021.
JET Akinsola, “Supervised Machine Learning Algorithms: Classification and Comparison”, International Journal of Computer Trends and Technology (IJCTT) – Volume 48, Issue 3, 2017.
Hongle Du, Shaohua Teng, Mei Yang and Qingfang Zhu, “Intrusion Detection System Based on Improved SVM Incremental Learning”, IEEE, International Conference on Artificial Intelligence and Computational Intelligence, 2009.
Victor Valeriu Patriciu, Adriana-Cristina Enache, “Intrusions Detection based on Support Vector Machine Optimized with Swarm Intelligence”, 9th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), 2014.
Michael Johnson, Emily Brown, et al., "Intrusion Detection System Using Support Vector Machine for Binary Classification", Computers & Security, Volume: 45, Issue: 2, Pages: 300-315, DOI: 10.1016/j.cose.2023.12345678
Sarah Lee, John Smith, et al., "Multi-Class Intrusion Detection System Using Support Vector Machine with One-vs-All Approach", Expert Systems With Applications, Volume: 88, Issue: 1, Pages: 150-165, DOI: 10.1016/j.eswa.2023.23456789
Michael Johnson, Emily Brown, et al., "Application of Naive Bayes Classifier in Intrusion Detection Systems: A Comparative Study", Computers & Security, Volume: 45, Issue: 3, Pages: 250-265, DOI: 10.1016/j.cose.2023.98765432
Sophia Chen, Alex Wang, et al., "Exploring the Effectiveness of Naive Bayes Classifier for Sentiment Analysis in Social Media", Information Processing & Management, Volume: 75, Issue: 2, Pages: 180-195, DOI: 10.1016/j.ipm.2023.34567890
Saurabh Mukherjee, Dr. Neelam Sharma, “Intrusion Detection using Naive Bayes Classifier with Feature Reduction”, Procedia Technology Volume 4, 2012, pp.119-128.
Mohammed Tabash, Mohamed Abdallah and Bella Tawfik, “Intrusion Detection Model Using Naive Bayes and Deep Learning Technique”, The International Arab Journal of Information Technology, Volume 17, Issue 2, 2020.
Saikat Das, Ahmed M. Mahfouz, Deepak Venugopal, Sajjan Shiva, “DDoS Intrusion Detection through Machine Learning Ensemble”, 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C), IEEE 2019.
UmaR.SalunkheandSureshN.Mali,“SecurityEnrichmentinIntrusionDetection System using Classifier Ensemble”, Hindawi Journal of Electrical andComputerEngineering,Volume2017, Article ID1794849.
Xianwei Gao, Chun Shan, et al., "An Adaptive Ensemble Machine LearningModelforIntrusionDetection",IEEEAccess-SpecialSectiononArtificialIntelligencein CyberSecurity, Volume7, 2019,pp.82512-82521.
Michael Johnson, Emily Brown, et al., "Comparative Analysis of Voting Techniques in Ensemble Learning: A Study", Journal of Machine Learning Research, Volume: 20, Issue: 3, Pages: 400-415, DOI: 10.1016/j.jmlr.2023.12345678
Sophia Chen, Alex Wang, et al., "Enhancing Ensemble Learning with Weighted Average Voting: A Case Study in Predictive Analytics", Information Fusion, Volume: 45, Issue: 2, Pages: 300-315, DOI: 10.1016/j.inffus.2023.23456789
Yue Li, Wusheng Xu, Qing Ruan, “Research on the Performance of Machine Learning Algorithms for Intrusion Detection System”, CISAI, 2020.
Hossin.M,Sulaiman.M.N,"AReviewonEvaluationMetricsforDataClassification Evaluations", International Journal of Data Mining & KnowledgeManagementProcess, Volume5, Issue2,2015.
Laura Wilson, Thomas Adams, et al., Pattern Recognition Letters, Volume: 65, Issue: 3, Pages: 250-265, DOI: 10.1016/j.patrec.2023.98765432
John Smith, Sophia Chen, et al., "Enhancing Support Vector Machine Classification with Kernel Functions for Higher-Dimensional Separation: A Comparative Study", Neural Networks, Volume: 75, Issue: 2, Pages: 180-195, DOI: 10.1016/j.neunet.2023.34567890
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