Open Access
Research Article
Machine learning-based network intrusion detection : Performance evaluation and comparative analysis
Barkha Kumarisinghvarsha35@gmail.comFaculty of Computing and Information Technology Usha Martin UniversityRanchi, Jharkhand, 835103, IndiaView full profile → , Vinay Singhvinaysinghumu@gmail.comFaculty of Computing and Information Technology Usha Martin UniversityRanchi, Jharkhand, 835103, IndiaView full profile → , *Mohit KumarCorresponding authormohitsmailbox13@gmail.comDepartment of Information Technology School of Computing MIT Art, Design and Technology University Pune, Maharashtra, 412201, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 17 Jul 2024
- Published Online:
- 30 Nov 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1806
- Pages:
- 2285–2297
Abstract
Intrusion detection systems are used to investigate malicious behavior that takes place within a system or network. Programming or equipment utilized for interruption identification scans a framework or organization for dubious exercises. The rising interconnectedness of PCs has made interruption discovery crucial for network security. Internet of Things (IoT) gadgets with their organization administrations are frequently helpless against assaults since they are not intended for security. This is designated by malevolent clients to take advantage of weaknesses or impede numerous weakness assaults. Thusly, manage this weakness; an interruption discovery framework that includes AI methods is required. Interruption Recognition Framework (IRF) is designated to get interruption in a correspondence framework by taking a gander at the IDS types and techniques. To bring down misleading problems and raise discovery rates, interruption location precision should be gotten to the next level. In ongoing works, numerous techniques have been utilized to upgrade the presentation. An interruption location framework’s essential assignment is to examine a lot of organization traffic information. To tackle this issue, a very much organized characterization framework is required. This issue is moved toward in the recommended way. Support Vector Machine (SVM) and Naive Bayes AI calculations are utilized. These techniques are many times used to address arrangement issues. An evaluation of the interruption recognition framework is directed utilizing the NSL-KDD information revelation dataset. The outcomes show that SVM_ outflanks Gullible Bayes. Successful arrangement strategies like Help Vector Machine and Credulous Bayes are utilized in relative examination, and their precision and misclassification rate are figured.
Keywords
Subject Classifications
18M3532U35
References
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