Secure authentication is essential in the cloud since anyone might attempt to enter into your account from anywhere on the internet. However, even after you've taken every precaution to secure it, someone may still gain access to your cloud administration layer, including the APIs that lead to the leakage of keys or accounts. Therefore, intrusion detection at the cloud layer, which is above the computing layer, is crucial. By implementing an authentication-based intrusion detection system, cloud computing platforms cannot only ensure that legidmate users are accessing resources and data on the cloud, also detect malicious activity that can cause leading cyber attacks, and other security incidents that can result in financial loss, reputational damage, and legal liabilities. To circumvent the above menthioned threats, this study proposed an Authentication Based Suspicious Behavior Detection System that to do intrusion detection at the cloud layer that explores the API. A knowledge-based (Kbase) dataset is used to detect the authentication phase allied attacks. Simulation practices of multiple cracking tasks demonstrate the applicability of the approach.

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