Anomaly Detection on AWS cloud by using Supervised Machine Learning

Cloud security has gained widespread popularity across various industries due to a variety of services that offer improved functionality, scalability, and affordability. However, the security of cloud environments has become increasingly critical due to the increasing number of attacks. Traditional methods of security monitoring often rely on predefined rules and thresholds which may not adapt well to evolving threats or complex attack patterns. An approach for detecting anomalies in the cloud by analyzing patterns in AWS (Amazon Web Service) CloudTrail logs has been presented. Although there are other cloud service providers available, AWS is chosen for anomaly detection due to its popularity. Analyzing such a vast number of logs without automation is complex, and detecting anomalies within this large dataset is a crucial task. Artificial intelligence (AI) is chosen for this task because it can spot patterns and anomalies that are too difficult to find manually, making detection more accurate and efficient. This paper examines the various supervised and unsupervised machine learning for accurate anomaly detection. By automating this process, the system aims to provide real-time alerts, enabling prompt responses to potential issues. The performance of the system is evaluated using metrics like precision, recall, and accuracy score. Our results demonstrate 97% detection accuracy. The results indicate that the proposed solution can effectively identify anomalies with high precision, demonstrating its potential to improve cloud security and operational management.