Biblio

With the dramatic increase in malicious software, the sophistication and innovation of malware have increased over the years. In particular, the dynamic analysis based on the deep neural network has shown high accuracy in malware detection. However, most of the existing methods only employ the raw API sequence feature, which cannot accurately reflect the actual behavior of malicious programs in detail. The relationship between API calls is critical for detecting suspicious behavior. Therefore, this paper proposes a malware detection method based on the graph neural network. We first connect the API sequences executed by different processes to build a directed process graph. Then, we apply Bert to encode the API sequences of each process into node embedding, which facilitates the semantic execution information inside the processes. Finally, we employ GCN to mine the deep semantic information based on the directed process graph and node embedding. In addition to presenting the design, we have implemented and evaluated our method on 10,000 malware and 10,000 benign software datasets. The results show that the precision and recall of our detection model reach 97.84% and 97.83%, verifying the effectiveness of our proposed method.

Ciphertext policy attribute-based encryption (CP-ABE) is a promising technology that offers fine-grained access control over encrypted data. In a CP-ABE scheme, any user can decrypt the ciphertext using his secret key if his attributes satisfy the access policy embedded in the ciphertext. Since the same ciphertext can be decrypted by multiple users with their own keys, the malicious users may intentionally leak their decryption keys for financial profits. So how to trace the malicious users becomes an important issue in a CP-ABE scheme. In addition, from the practical point of view, users may leave the system due to resignation or dismissal. So user revocation is another hot issue that should be solved. In this paper, we propose a practical CP-ABE scheme. On the one hand, our scheme has the properties of traceability and large universe. On the other hand, our scheme can solve the dynamic issue of user revocation. The proposed scheme is proved selectively secure in the standard model.