A MACHINE LEARNING APPROACH FOR INTRUSION DETECTION

Abstract

 In the rapidly evolving digital landscape, ensuring the security of computer systems and networks has become a paramount concern. Traditional methods often struggle to keep pace with the increasing complexity and diversity of cyber threats. In the context of cybersecurity and network analysis, the crucial tools for identifying and responding to unauthorized access, malicious activities, and potential threats within a network or system cannot take place. The significance of intrusion detection is underscored by the escalating frequency and sophistication of cyberattacks that can lead to data breaches, service disruptions, and financial losses. The XGBoost algorithm's ability to handle complex, imbalanced datasets and its exceptional performance in various domains make it a promising candidate for improving intrusion detection accuracy. To evaluate the efficacy of the XGBoost algorithm, extensive experiments are conducted using the KDDCup99 dataset. This dataset represents diverse network traffic scenarios and intrusion types, providing a comprehensive evaluation environment. The Random Forest algorithm, known for its robustness in handling classification tasks, is selected as a baseline for comparison. The performance of the XGBoost algorithm is assessed using multiple performance metrics, including accuracy, precision, recall, F1-score. The outcomes highlight the potential advantages of utilizing XGBoost for intrusion detection.