Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2024 International Conference on Decision Aid Sciences and Applications (DASA) |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Pages | 1-5 |
| Number of pages | 5 |
| ISBN (Electronic) | 9798350369106 |
| ISBN (Print) | 979-8-3503-6911-3 |
| DOIs | |
| Publication status | Published - 17 Jan 2025 |
| Event | 2024 International Conference on Decision Aid Sciences and Applications (DASA) - Manama, Bahrain Duration: 11 Dec 2024 → 12 Dec 2024 |
Conference
| Conference | 2024 International Conference on Decision Aid Sciences and Applications (DASA) |
|---|---|
| Country/Territory | Bahrain |
| City | Manama |
| Period | 11/12/24 → 12/12/24 |
Keywords
- Applications
- Machine Learning (ML)
- NIDS
- SMOTE
- WSN Security
- Wireless Sensor Networks (WSN)