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Mobile cloud computing uses cloud computing to deliver applications to mobile devices. These applications can be delivered among different devices with different operating systems, computing tasks, and data storage capabilities, adopting D2D communication mode. However, because the application delivery process covers three entities, namely user-environmental-service, trusted problems are ubiquitous. Therefore, before cloud provides substantive services, how to identify the trusted degree of user identity and its behaviors for D2D communications is the core problem. First, from the perspective of user trustability, this paper proposed an analysis method to user abnormal behaviors for D2D communications in mobile cloud environment. In this method, user behavior is normalized to a &#x201C;user sequence operation&#x201D;identity fragment with the same length, offset, and amplitude. The hierarchical matching method and the blacklist mechanism are used to determine whether the user behavior for D2D communications is beyond the scope of trusted tolerance. Second, considering that the user sequence behavior step is a complex graph structure with continuous dynamic growth, this paper proposed a pattern growth method based on maximum and right-most path extension for autonomous optimization. At last, the experimental results showed that the classification accuracy under the KDD CUP99 data set and real network environment was 94.8% and 90.2%, respectively, which was 5.3% and 6.9% higher than the traditional methods. In addition, it can be seen from the experimental results that this scheme could significantly improve the recognition speed.

An Anomaly Recognition and Autonomic Optimization Method to User’s Sequence Behaviors for D2D Communications in MCC