Enhancing Intrusion Detection with Principal Component Analysis and Random Forest

With the rising number of cybersecurity attacks, intrusion detection systems (IDS) play a vital role in spotting unauthorized access and lowering security risks. This study presents a framework that integrates principal component analysis (PCA) for dimensionality reduction with random forest (RF) for classification tasks. The proposed model is evaluated using the NSL-KDD dataset, showing notable gains in detection accuracy, reduced error rates, and faster processing compared to common models like SVM and Na?ve Bayes. Results show that the proposed approach achieves 96.78% accuracy while keeping the error rate at 0.21%.