Biblio

The user's access history can be used as an important reference factor in determining whether to allow the current access request or not. And it is often ignored by the existing access control models. To make up for this defect, a Dynamic Trust - game theoretic Access Control model is proposed based on the previous work. This paper proposes a method to quantify the user's trust in the cloud environment, which uses identity trust, behavior trust, and reputation trust as metrics. By modeling the access process as a game and introducing the user's trust value into the pay-off matrix, the mixed strategy Nash equilibrium of cloud user and service provider is calculated respectively. Further, a calculation method for the threshold predefined by the service provider is proposed. Authorization of the access request depends on the comparison of the calculated probability of the user's adopting a malicious access policy with the threshold. Finally, we summarize this paper and make a prospect for future work.

Binary function similarity detection has been an important problem in binary analysis. Recently, several deep learning based techniques show promising results and achieve state of the art performance. In despite of effectiveness, some of them adopt complex network structures which result in slow convergence speed. To reduce the structure complexity, this paper proposes a simple neural network structure based on Bi-RNN(Bidirectional Recurrent Neural Network). To evaluate the effectiveness of proposed method, this paper conducts experiments on both single-architecture and cross-architecture function similarity detection over OpenSSL library. Experimental results show that our approach achieves higher AUC metric and faster convergence speed than GNN(Graph Neural Network)-based techniques. To explore the effectiveness of GNN, another experiment is conducted to explore the impact of the number of GNN layers on performance and the result shows that when combined semantic information, existing GNN-based methods may reduce performance.