Optimizing Network Intrusion Detection with Hybrid DTRJ Model: A Data Mining Approach

This research delves into the development and implementation of the Hybrid Decision Tree-based Random Forest and J48 (DTRJ) Model for network intrusion detection, employing a data mining approach to significantly enhance feature selection and classification algorithms. With the burgeoning complexity of cyber threats, traditional intrusion detection systems (IDS) face challenges in effectively monitoring and securing networks. Leveraging the NSL-KDD dataset, this study introduces sophisticated data preprocessing and feature selection methods, including the Modified FireFly Algorithm (MFFA), Chi-square, and Bayes Network, to refine the detection process. The core of the research is the Hybrid DTRJ Model, an innovative ensemble classifier that merges the strengths of Decision Tree-based Random Forest and J48 algorithms, aiming to improve detection accuracy and minimize false positives. A comprehensive performance evaluation using various metrics confirms the model's effectiveness in identifying network intrusions, setting a new benchmark for IDS efficiency and reliability. This research highlights the critical role of advanced data mining techniques in the future of network security, offering a robust solution to adapt to and mitigate evolving cyber threats.