Attribute-based encryption (ABE) is a promising cryptographic tool for data owner (DO) to realize fine-grained date sharing in the cloud computing. In the encryption of most existing ABE schemes, a substantial number of modular exponentiations are often required; the computational cost of it is growing linearly with the complexity of the access policy. Besides, in the most existing ABE with outsourced decryption, the computation cost of generating transformation key is growing linearly with the number of attributes associated with user private key; these computations are prohibitively high for mobile device users, which becomes a bottleneck limiting its application. To address the above issues, we propose a secure outsourcing algorithm for modular exponentiation in one single untrusted server model and a new method to generate the transformation key. Based on these techniques and Brent Waters's ciphertext-policy ABE scheme, we propose an ABE scheme with verifiable outsourced both encryption and decryption, which can securely outsource encryption and decryption to untrusted encryption service provider (ESP) and decryption service provider (DSP), respectively, leaving only a constant number of simple operations for the DO and eligible users to perform locally. In addition, both DO and the eligible users can check the correctness of results returned from the ESP and the DSP with a probability, respectively. Finally, we provide the experimental evaluation and security analysis of our scheme, which indicates that our construction is suitable for the mobile environment.

Webshell is a kind of backdoor programs based on web services. Network based detection could monitor the request and response traffic to find abnormal behaviors and detect the existence of Webshell. Some machine learning and deep learning methods have been used in this field, but the current methods need to be further explored in discovering new attacks and performance. In order to detect large-scale unknown Webshell events, we propose a Webshell traffic detection model combining the characteristics of convolutional neural network (CNN) and long short term memory network (LSTM). At the same time, we propose a character level traffic content feature transformation method. We apply the method in our proposed model and evaluate our approach on a Webshell detection testbed. The experiment result indicates that the model has a high precision rate and recall rate, and the generalization ability can be guaranteed.

Cloud storage attracts a lot of clients to join the paradise. For a high data availability, some clients require their files to be replicated and stored on multiple servers. Because clients are generally charged based on the redundancy level required by them, it is critical for clients to obtain convincing evidence that all replicas are stored correctly and are updated to the up-to-date version. In this paper, we propose a dynamic proof of data possession and replication (DPDPR) scheme, which is proved to be secure in the defined security model. Our scheme shares a single authenticated tree across multiple replicas, which reduces the tree's storage cost significantly. Our scheme allows for batch verification for multiple challenged leaves and can verify multiple replicas in a single batch way, which considerably save bandwidth and computation resources during audit process. We also evaluate the DPDPR's performance and compare it with the most related scheme. The evaluation results show that our scheme saves almost 66% tree's storage cost for three replicas, and obtains almost 60% and 80% efficiency improvements in terms of the overall bandwidth and computation costs, respectively, when three replicas are checked and each challenged with 460 blocks.