A Deep Learning-Based Trust Assessment Method for Cloud Users

Abstract

Attacks launched from the inside of the cloud are threats not only to the cloud users but also to the cloud infrastructures. Although with trusted computing the cloud service providers can guarantee the trust and security of the cloud environment for the users, the trustworthiness of users is not properly assessed. Inspired by the concept of variable trust, the main contribution of this paper is that we propose a trust assessment method for cloud users based on deep learning. Firstly, we extract users' activities from system logs and employ stacked LSTM (long short-Term memory) neural network to model normal activity patterns to build trust profiles for different users. Secondly, the trust profile is capable of predicting future behavioural actions of the specific user, and by calculating the similarity between predicted actions and actual actions the trustworthiness of the user will be assessed with a baseline to detect the trust state of the cloud user dynamically. And in the end, we design and conduct experiments on a public dataset. The results of experiments indicate that when the user is in abnormal state, there are notable differences between predicted actions and user's actual actions, which proves the efficiency of the proposed method.