Anomaly Based Network Intrusion Detection System Using ML

Network security has become one of the most critical aspects of modern computer systems. New cyber threats emerge every day, and many of them can evade traditional security mechanisms. Anomaly-based intrusion detection helps address this challenge by identifying unexpected or irregular behavior that could signal an attack. This project develops a machine-learning-driven intelligent detection system to spot anomalies in network traffic. By training models such as Random Forest and XGBoost on real-world datasets, the system learns to identify deviations from normal activity, including abnormal traffic volumes, access during unusual hours, or unexpected protocol usage. It focuses on effective feature extraction, handling imbalanced data, and supporting both binary and multiclass attack classification. The final system is designed to be scalable, interpretable, and dependable, enabling early detection of potential threats before they cause any damage.

Computer Science / Information Security

1. Talukder, M. A., Islam, M. M., Uddin, M. A., Hasan, K. F., Sharmin, S., Alyami, S. A., & Moni, M. A. (2024). Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction. Journal of Big Data, 11(33). [Google Scholar] [Crossref]

2. Liu, L., Wang, P., Lin, J., & Liu, L. (2021). Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning and Deep Learning. IEEE Access, 9, 7549–7563. [Google Scholar] [Crossref]

3. Zhang, Y., Kyriakopoulos, K., Yang, Q., Ghafir, I., Lambotharan, S., &AsSadhan, B. AnomalyBased Network Intrusion Detection Using SVM. Xidian University and Loughborough University. \[IEEE Xplore, 2025] [Google Scholar] [Crossref]

4. García-Teodoro, P., Díaz-Verdejo, J., Maciá-Fernández, G., & Vázquez, E. (2009). Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security, 28(1–2), 18– 28. [Google Scholar] [Crossref]

5. Goel, S., Guleria, K., & Panda, S. N. (2022). Anomaly based Intrusion Detection Model using Supervised Machine Learning Techniques. IEEE. [Google Scholar] [Crossref]

6. Yihunie, F., Abdelfattah, E., & Regmi, A. (2023). Applying Machine Learning to Anomaly-Based Intrusion Detection Systems. Sacred Heart University & Ramapo College of New Jersey. [Google Scholar] [Crossref]

7. Nurul Fauzi, Fazmah Arif Yulianto, and Hilal Hudan Nuha, “The Effectiveness of Anomaly-Based Intrusion Detection Systems in Handling Zero-Day Attacks Using AdaBoost, J48, and Random Forest Methods”, IEEE,1-6. [Google Scholar] [Crossref]

8. Sara A. Althubiti, Eric Marcell Jones Jr., and Kaushik Roy,“LSTM for Anomaly-Based Network Intrusion Detection”,IEEE ,128-132 [Google Scholar] [Crossref]

9. Abhishek Meena, Deepanshu Nigam, Deepesh Sharma, and Anamika Chauhan,“Anomaly Based Intrusion Detection for IoT: A Deep Learning Approach”, In Proceedings of the *3rd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 2021, pp. 1349–1356. [Google Scholar] [Crossref]

10. Riccardo Colelli, Filippo Magri, Stefano Panzieri, and Federica Pascucci,“Anomaly-Based Intrusion Detection System for Cyber-Physical System Security”, In Proceedings of the 29th Mediterranean Conference on Control and Automation (MED), 2021, pp. 428–434. [Google Scholar] [Crossref]

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