Enhancing Network Intrusion Detection using Hybrid Machine Learning and Deep Learning Approaches: A Comparative Analysis with the HIKARI-2021 Dataset

Abstract

This thesis presents an in-depth analysis of machine learning (ML) and deep learning (DL) methodologies for network intrusion detection, utilizing the HIKARI-2021 dataset. By leveraging models such as Random Forest, XG Boost, LSTM, and GRU, the study aimed to identify and classify malicious activities within network traffic. The models' performance was assessed primarily based on accuracy, as well as confusion matrix evaluations. Preliminary results indicate Random Forest achieved an accuracy of 93.77%, XG Boost attained 93.02%, LSTM reached 92.48%, and GRU reported 92.50%. These results were then compared to benchmark models from the literature, which achieved accuracies ranging from 98% to 99%. Through this comparative analysis, the research emphasizes the strengths, weaknesses, and the potential of each model in real-world scenarios. Notably, while the employed models showcased commendable performance, benchmark models exhibited slightly superior results, suggesting further room for model optimization and feature engineering. This research offers valuable insights into the evolving landscape of network security and sets the stage for further exploration in enhancing intrusion detection mechanisms.