Enhanced Cyber Threat Detection System Leveraging Machine Learning Using Data Augmentation
Umar Iftikhar, Syed Abbas Ali · International Journal of Advanced Computer Science and Applications · 2025
In the modern era of cyber security, cyber-attacks are continuously evolving in terms of complexity and frequency. In this context, organizations need to enhance Network Intrusion Detection Systems (NIDS) for anomaly detection. Although the existing Machine Learning models are in place to cater to the situations but new challenges emerge rapidly which affects the performance and efficiency of existing models specifically the unreachability of large datasets and unorganized data. This results in degraded efficiency for the identification of complex attacks. In this paper, data augmentation has been done of NSL-KDD which is a standard dataset for Intrusion Detection Systems (IDS) specifically for IoT-based devices. The improvement in performance and efficiency of NIDS has been performed by training the augmented dataset using the K-Nearest Neighbor (KNN) ML model.