AN INVESTIGATION INTO THE EFFECTIVENESS OF AI-BASED INTRUSION DETECTION SYSTEMS IN MODERN NETWORK ENVIRONMENTS

Abstract

The rapid growth of complex and distributed network environments has intensified the need for advanced security mechanisms capable of detecting sophisticated cyber threats. Traditional intrusion detection systems (IDS) often struggle to identify emerging and zero-day attacks due to their reliance on predefined signatures and limited adaptability. This study investigates the effectiveness of artificial intelligence (AI)-based intrusion detection systems in enhancing network security within modern infrastructures. It examines the application of machine learning and deep learning techniques, including supervised and unsupervised models, for real-time threat detection and classification. The research adopts a comparative analytical approach, evaluating AI-based IDS against conventional systems in terms of detection accuracy, false positive rates, scalability, and response time. Experimental findings indicate that AI-driven models significantly improve detection capabilities by identifying complex attack patterns and adapting to evolving threats. However, challenges such as high computational cost, data imbalance, and model interpretability remain critical concerns. The study highlights the potential of integrating AI with hybrid detection frameworks to achieve more robust and efficient security solutions. Overall, the research contributes to the ongoing development of intelligent cyber-security systems by providing insights into the practical effectiveness and limitations of AI-based IDS in contemporary network environments.