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.
Khoh et al. (Sun,) studied this question.