Performance-Based Threat Detection in Cloud Environments

Cloud computing environments, with their vast scale and dynamic nature, are prime targets for cyber- attacks. The effectiveness of traditional security systems is often limited due to high false-positive rates, delayed response times, and resource constraints. Performance-based threat detection (PB-TD) represents an innovative approach that focuses on leveraging system performance metrics (e.g., CPU usage, memory utilization, network traffic) to identify anomalies indicative of potential security threats. This paper explores the application of performance-based metrics to enhance the accuracy and efficiency of threat detection in cloud environments. By analyzing real-time system performance data, PB-TD enables faster identification of security incidents, reduces the computational overhead of conventional intrusion detection systems (IDS), and optimizes resource usage. The study investigates the effectiveness of PB-TD in cloud environments, focusing on performance gains, detection rates, and real-time scalability. The findings suggest that performance-based methods significantly enhance threat detection, especially in resource-constrained cloud settings.