Cyber Intrusion Detection Using Machine Learning Techniques

Abstract

Cyber Intrusions Are Becoming Increasingly Complex And Sophisticated, Making Them Difficult To Detect Using Conventional Rule-based And Signature-driven Security Mechanisms. The Rapid Growth Of Network Traffic And Evolving Attack Patterns Demand Intelligent, Adaptive, And Real-time Intrusion Detection Solutions. This Project Proposes A Real-time Intrusion Detection System Based On An Ensemble Learning Framework That Integrates Random Forest, XGBoost, And Deep Neural Network Models To Enhance Detection Accuracy And Robustness. Live Network Traffic Is Continuously Captured And Analyzed At Fixed Intervals To Enable Timely Identification Of Malicious Activities. Meaningful Network Features Are Extracted From The Traffic Data And Processed In Parallel By All Three Learning Models, Allowing The System To Leverage The Strengths Of Both Machine Learning And Deep Learning Approaches. A Stacking-based Meta-classifier Is Employed To Intelligently Combine The Individual Model Predictions, Thereby Reducing False Positives And Improving Overall Classification Performance. The Proposed System Effectively Classifies Network Traffic As Either Normal Or Intrusive And Further Identifies The Specific Category Of Cyber-attacks. In Addition, Real-time Alerts And Detailed Log Reports Are Generated To Facilitate Rapid Response And Incident Analysis. Experimental Evaluation Demonstrates That Theproposed Ensemble-based Intrusion Detection System Achieves Improved Accuracy, Reliability, And Practical Applicability, Making It Suitable For Deployment In Real-world Network Security Environments.

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