An Efficient ML-Based Model for Network Intrusion Detection System
In the modern era, cyber-security is a solution to protect systems, networks, and programs from different attacks. A network intrusion detection system (NIDS) is a secure system that analyses vulnerabilities and security attacks in cyberspace which is used to find malicious activity. Machine Learning (ML) techniques are frequently used to solve anomaly-related problems. Therefore, a data-driven machine learning model is proposed to detect the issues related to NIDS through the benchmark dataset. The study's results depict that the proposed model outperforms the contemporary model.
Authors
- Pradip Sinha (ORCID: https://orcid.org/0000-0002-9202-6050)
- Rajkumar Singh Rathore (ORCID: https://orcid.org/0000-0003-4571-1888)
- Vandana Rathore (ORCID: https://orcid.org/0000-0003-0788-0760)
- Shiv Prakash (ORCID: https://orcid.org/0000-0001-7610-3004)
- Tiansheng Yang (ORCID: https://orcid.org/0000-0001-7833-5386)
- Abhishek Singh (ORCID: https://orcid.org/0009-0006-7537-8173)
- Sudhanshu Kumar Jha (ORCID: https://orcid.org/0000-0002-1613-3292)
- Rahul Mishra
Institutions
- Dr. A.P.J. Abdul Kalam Technical University (IN)
- University of South Wales (GB)
- University of Allahabad (IN)
- Cardiff Metropolitan University (GB)
Publication Details
- Published
- 2024-12-11
- DOI
- https://doi.org/10.1109/dasa63652.2024.10836316
- Citations
- 6
- Primary Topic
- Network Security and Intrusion Detection
- Type
- article
- Field-Weighted Citation Impact
- 2.09