Citation

Khoh, Wee How and Teoh, Zhi Lin Sarah and Yap, Hui Yen and Teh, Pin Shen (2026) AI-Driven Intrusion Detection System for Network Security. Journal of Informatics and Web Engineering, 5 (2). p. 250. ISSN 2821-370X

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Abstract

In the evolving landscape of cybersecurity, Intrusion Detection Systems (IDS) play a vital role in safeguarding computer networks against malicious activity. Traditional signature-based IDS approaches are increasingly ineffective in detecting novel, complex, or zero-day attacks due to their reliance on predefined rules. To overcome these limitations, this study proposes an AI-driven IDS that integrates both classical machine learning and modern deep learning techniques. The framework introduces and compares three models such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), and a hybrid CNN-SVM model. The system is designed to analyze network traffic patterns and classify them as either benign or malicious, enhancing detection capabilities through intelligent feature learning and classification. Two widely recognized benchmark datasets are used to train and validate the models, ensuring the system's applicability to a variety of network environments. Furthermore, the research includes the development of a real-time detection component that incorporates live packet capture, feature extraction, and dynamic visualization via a dashboard interface. This paper contributes to the field by demonstrating how hybrid AI models can effectively address the challenges of network intrusion detection. The study emphasizes the importance of combining traditional and deep learning approaches to build scalable, adaptive, and accurate intrusion detection systems for modern network infrastructures.

Item Type:	Article
Uncontrolled Keywords:	Intrusion Detection System, Machine Learning, Deep Learning, Support Vector Machine, Convolutional Neural Network, Hybrid Model, Real-Time Detection
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	09 Jul 2026 03:33
Last Modified:	09 Jul 2026 03:33
URII:	http://shdl.mmu.edu.my/id/eprint/16335

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