Classifying Malicious Thread Behavior in PaaS Web Services

Abstract

Multitenant structure of PaaS cloud delivery model allows customers to share the platform resources in the cloud. However, this structure requires a strong security mechanism that isolates customer applications to prevent interference between different applications. In this paper, a malicious thread behavior detection framework using machine learning algorithms is proposed to classify whether user requests are malicious. The framework uses thread metrics of worker threads and N-Gram frequencies of operations as its features. Test results are evaluated on a real-life scenario using Random Forest, Adaboost and Bagging ensemble learning algorithms and evaluated using different accuracy metrics. It is found that the malicious request detection accuracy of the proposed system is 87.6%.