Implementing Adaptive Security Monitoring: Aws Cloudwatch and Advanced Threat Detection Techniques

Abstract

Traditional monitoring systems can fail to provide timely and precise threat identification as cloud infrastructures get more complicated and targeted by advanced cyber attacks. This paper looked at how adaptive security monitoring may be implemented by improving AWS CloudWatch with sophisticated threat detection methods including machine learning-based anomaly detection, behavioral analytics, and automated remediation. Key performance measures including detection accuracy, reaction time, system overhead, and scalability were used to compare conventional CloudWatch monitoring with the adaptive system across a simulated AWS environment. The findings indicated that the adaptive system greatly increased threat detection accuracy, lowered response time by more than 78%, and kept strong performance under high-load settings with only little extra resource use. These results underline the need of smart, automated monitoring systems in protecting cloud environments against changing security threats.