Adaptive Network Intrusion Detection Using Fine-Tuned Random Forest with PCA

In the dynamic realm of network security, intrusion detection systems (IDS) must evolve to accurately detect both established and novel threats. This research presents an adaptive intrusion detection system that integrates Principal Component Analysis (PCA) with an optimized Random Forest (RF) classifier to improve detection precision and efficacy. PCA is utilized to diminish dimensionality and remove superfluous features, facilitating expedited processing and enhanced model efficacy. The RF classifier is optimized via hyperparameter tuning, resulting in enhanced anomaly detection in network traffic. Experimental findings on benchmark intrusion detection datasets, including KDDCUP, indicate that the proposed system attains an accuracy of 96.8% and a low false-positive rate of 3.2%, surpassing conventional methods such as SVM and Decision Trees (DT). This hybrid method demonstrates its capability as a scalable and dependable solution for contemporary network intrusion detection.