Intrusion Detection in Cybersecurity: A Machine Learning Classifier with Optimized Feature Selection

Concerns about security and privacy were heightened by the increasing dependence on interconnected networks, underscoring the vital role intrusion detection systems (IDS) play in protecting private data. As essential components of modern cybersecurity frameworks, IDS are designed to detect and prevent malicious activities. However, traditional systems face challenges such as handling high-dimensional data, addressing class imbalance, and detecting unknown threats due to the rising sophistication of cyberattacks and growing network traffic. This study proposes a Selective Features Optimized Machine Learning (ML) Classifier that improves IDS performance by utilizing Support Vector Machine (SVM) classification and Genetic Algorithm (GA) based feature selection. The model achieves impressive results, with accuracy of 96%, precision of 95.4%, and recall of 94%, demonstrating its reliability and effectiveness in identifying malicious network traffic. By integrating GA driven feature selection with SVM classification, the proposed system significantly improves threat detection, offering robust solution for real world cybersecurity challenges.