With Android’s dominant position within the current smartphone OS, increasing number of malware applications pose a great threat to user privacy and security. Classification algorithms that use a single feature usually have weak detection performance. Although the use of multiple features can improve the detection effect, increasing the number of features increases the requirements of the operating environment and consumes more time. We propose a fast Android malware detection framework based on the combination of multiple features: FAMD (Fast Android Malware Detector). First, we extracted permissions and Dalvik opcode sequences from samples to construct the original feature set. Second, the Dalvik opcodes are preprocessed with the N-Gram technique, and the FCBF (Fast Correlation-Based Filter) algorithm based on symmetrical uncertainty is employed to reduce feature dimensionality. Finally, the dimensionality-reduced features are input into the CatBoost classifier for malware detection and family classification. The dataset DS-1, which we collected, and the baseline dataset Drebin were used in the experiment. The results show that the combined features can effectively improve the detection accuracy of malware that can reach 97.40% on Drebin dataset, and the malware family classification accuracy can achieve 97.38%. Compared with other state-of-the-art works, our framework achieves higher accuracy and lower time consumption.

With the rapid development of network and mobile communication, intelligent terminals such as smartphones and tablet computers have changed people’s daily life and work. However, malware such as viruses, Trojans, and extortion applications have introduced threats to personal privacy and social security. Malware of the Android operating system has a great variety and updates rapidly. Android malware detection is faced with the problems of high feature dimension and unsatisfied detection accuracy of single classification algorithms. In this work, an Android malware detection framework GA-StackingMD is presented, which employs Stacking to compose five different base classifiers, and Genetic Algorithm is applied to optimize the hyperparameters of the framework. Experiments show that Stacking could effectively improve malware detection accuracy compared with single classifiers. The presented GA-StackingMD achieves 98.43% and 98.66% accuracies on CIC-AndMal2017 and CICMalDroid2020 data sets, which shows the effectiveness and feasibility of the proposed method.

In the era of big data, more and more users store data in the cloud. Massive amounts of data have brought huge storage costs to cloud storage providers, and data deduplication technology has emerged. In order to protect the confidentiality of user data, user data should be encrypted and stored in the cloud. Therefore, deduplication of encrypted data has become a research hotspot. Cloud storage provides users with data sharing services, and the sharing of encrypted data is another research hotspot. The combination of encrypted data deduplication and sharing will inevitably become a future trend. The current better-performing updateable block-level message-locked encryption (UMLE) deduplication scheme does not support data sharing, and the performance of the encrypted data de-duplication scheme that introduces data sharing is not as good as that of UMLE. This paper introduces the ciphertext policy attribute based encryption (CP-ABE) system sharing mechanism on the basis of UMLE, applies the CP-ABE method to encrypt the master key generated by UMLE, to achieve secure and efficient data deduplication and sharing. In this paper, we propose a permission verification method based on bilinear mapping, and according to the definition of the security model proposed in the security analysis phase, we prove this permission verification method, showing that our scheme is secure. The comparison of theoretical analysis and simulation experiment results shows that this scheme has more complete functions and better performance than existing schemes, and the proposed authorization verification method is also secure.