Advanced Machine Learning Approach to Handle Code Injection Attacks in Cloud Computing

Abstract

Cloud computing is one significant paradigm in business oriented applications; it is an emerging technology to believe clients to store their in secured format to resource provisioning with other users. There are different types of enhanced security mechanisms on earlier users present in distributed environment. Aggregated Key Management and Cryptosystem (AKM&C) is one of the approaches to provide security in multi file sharing with single aggregate key in distributed environment. In web oriented distributed environment, code injection related attacks were appeared to access information stored in web data server developed in HTML and CSS for the implementation of different out sourced data in cloud. So that in this paper, we propose Machine Learning with Cross Script Code  based approach (MLCSCA) to handle and detect code injection related attacks on cloud computing. In this approach, detection of XSS, code injection related attacks is performed based on URLs, web page and cloud resource data. Data set is prepared based on web data available in storage server with different parameters. Evaluate the efficiency of the proposed approach to compare with existing approaches like AKM&C and others. Evaluation results show effective performance with respect to accuracy and other parameter results in distributed environment