Development of Hybrid Intrusion Detection System with Combinations of Computational Intelligence Models

Abstract

In the ever-evolving cybersecurity landscape, the development of a comprehensive intrusion detection system remains paramount. With cyber threats becoming increasingly sophisticated and unpredictable, the methodology proposed in this study presents a significant advancement, offering a more proactive and adaptable defense mechanism. This study addresses the critical need for an effective intrusion detection system in the dynamic cybersecurity landscape. With evolving cyber threats posing substantial challenges, a novel approach is introduced, combining fuzzy logic and deep learning methodologies. The primary focus of this thesis is to enhance intrusion detection accuracy and adaptability. It achieves this by integrating fuzzy logic, known for its capability to handle uncertain and imprecise data, and advanced machine learning and deep learning techniques. Fuzzy logic is introduced to improve intrusion detection accuracy by allowing for the incorporation of expert-defined rules. Additionally, machine learning algorithms autonomously identify patterns within extensive network traffic data, while deep learning, with its hierarchical feature extraction and representation learning, equips the system to tackle the intricacies and variations in modern data. newlineThe outcomes of this study demonstrate substantial enhancements in intrusion detection accuracy compared to conventional methods. Notably, this approach effectively bridges gaps left by traditional intrusion detection systems and showcases its adaptability in countering evolving cyber threats. This thesis comprises several key chapters, including an introduction, literature review, methodology, newline newlineexperimental results and discussion, conclusion and future work. Each chapter contributes to a comprehensive understanding of the research and its implications. This study provides compelling evidence of the effectiveness and performance of the proposed framework, marking it as a valuable contribution to the field of intrusion detection.