A Comprehensive Analysis of Intrusion Detection System in Cloud Computing using Artificial Intelligence Technique

Intrusion Detection Systems (IDS) comprise of devices utilized for network monitoring to detect unusual activities that deviate from normal functionality and lead to policy violation. However, ensuring both privacy and security is a challenging task. Monitoring security operations becomes complex due to the presence of a large number of CC devices, their heterogeneity, and limitations in technical specifications, which lead to inefficiencies in security operations. To address this issue, Intrusion Detection Systems (IDS) are introduced to ensure a secure network against various vulnerabilities and threats. The integration of robust Machine Learning (ML) and Deep Learning (DL)-based techniques becomes crucial to address various security challenges. ML and DL-based IDS methods are fundamental in securing cloud environments and networks. The IDS monitors network traffic and system activities, effectively mitigating security threats caused by the rapid growth in network traffic. Additionally, the ML and DL techniques enhance the detection of known and unknown attacks, including zero-day attacks, thereby reducing false positive rates. The performance of ML and DL models is typically evaluated in terms of the metrics of accuracy, recall, precision, and F1-Score.