A Review of Resource Optimization Techniques in Intrusion Detection Systems

Intrusion Detection Systems (IDS) are critical components in ensuring the security of modern network infrastructures, providing real-time detection and mitigation of malicious activities. However, these systems are often challenged by limited computational resources, high false-positive rates, and inefficiencies in handling large volumes of data. Resource optimization techniques have emerged as a vital area of research aimed at enhancing the efficiency and accuracy of IDS implementations. This review systematically analyzes various resource optimization strategies employed in IDS. The paper discusses the applicability, advantages, limitations, and performance impacts of these techniques across different intrusion detection scenarios. Finally, future research directions are proposed, highlighting the potential integration of advanced machine learning methods and real-time adaptive optimization methods to further improve IDS efficiency and reliability.