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With The Rapid Growth Of Digital Communication And Increasing Sophistication Of Cyberattacks, Intrusion Detection Systems (IDS) Have Become Essential For Safeguarding Computer Networks. Traditional IDS Often Face Challenges Such As High Dimensionality Of Data, Redundant Features, And Reduced Detection Accuracy When Dealing With Large-scale Traffic. To Address These Issues, This Work Proposes An Intrusion Detection Framework That Integrates Principal Component Analysis (PCA) With A Random Forest Classifier. PCA Is Employed As A Dimensionality Reduction Technique To Eliminate Noise And Redundancy From Network Traffic Features, Thereby Improving Computational Efficiency. The Refined Feature Set Is Then Classified Using The Random Forest Algorithm, Known For Its Robustness, Scalability, And Ability To Handle Complex, Non-linear Relationships In Data. Experimental Evaluation Demonstrates That The Proposed PCA–Random Forest Approach Enhances Detection Accuracy, Reduces False Positive Rates, And Achieves Faster Processing Compared To Conventional Methods. This Hybrid Model Provides An Effective And Reliable Solution For Real-time Intrusion Detection In Modern Network Environments.