Insider Attack Monitoring and Secured Data Transfer Using KPCC and BiO-ELSTM in Cloud Computing

Abstract

Handling of User's data effectively in the cloud has become a crucial task. Monitoring insider attacks in the cloud and securely transferring cookie files have not been concentrated in the existing works. Insider threats pose significant risks to data security in cloud environments, particularly during data transfers between public and private/hybrid clouds. Existing methodologies often fail to adequately monitor these threats and secure data, leading to vulnerabilities such as cookie attacks and data integrity issues. This paper proposes a novel framework that integrates the Cyclic permutation-based Hirose-Streebog (CH-Streebog) algorithm for secure logins and hashcode verification, alongside the KPCC method for securing cookie files. Additionally, the WS-Fuzzy method is employed for optimal virtual machine selection, and the BiO-ELSTM classifier is utilized for effective insider threat monitoring, aiming to reduce false alarm rates. The proposed framework demonstrates impressive performance metrics, achieving a precision of 98.95%, recall of 98.02%, f-measure of 98.48%, accuracy of 98.26%, and specificity of 98.78%. These results indicate a significant improvement in insider threat detection and secure data handling in cloud environments compared to existing methods.