Development of an Intrusion Detection System using ANOVA Feature Selection and Support Vector Machine Algorithms

The escalating sophistication and frequency of cyber-attacks have necessitated the development of more advanced intrusion detection systems (IDS). This research presents the development of an innovative IDS employing Analysis of Variance (ANOVA) for feature selection and a Support Vector Machine (SVM) algorithm for intrusion detection. The aim is to enhance detection accuracy while reducing computational overhead. ANOVA was used to identify significant features from vast and complex network traffic data, simplifying the high-dimensional data and improving the detection system’s efficiency. The selected features are then classified using the Radial Basis Function (RBF) SVM algorithm, renowned for its high accuracy and robustness in handling high-dimensional data. A comparative analysis with existing IDS models demonstrates the improved efficiency and accuracy of the proposed model. This work provides an advanced methodology for cyber security, contributing to the ever-evolving battle against cyber threats.