Recently, security policies and behaviour detection methods have been proposed to improve the security of blockchain by many researchers. However, these methods cannot discover the source of typical behaviours, such as the malicious applications in the blockchain environment. Android application is an important part of the blockchain operating environment, and machine learning-based Android malware application detection method is significant for blockchain user security. The way of constructing features in these methods determines the performance. The single-feature mechanism, training classifiers with one type of features, cannot detect the malicious applications effectively which exhibit the typical behaviours in various forms. The multifeatures fusion mechanism, constructing mixed features from multiple types of data sources, can cover more kinds of information. However, different types of data sources will interfere with each other in the mixed features constructed by this mechanism. That limits the performance of the model. In order to improve the detection performance of Android malicious applications in complex scenarios, we propose an Android malicious application detection method which includes parallel feature processing and decision mechanism. Our method uses RGB image visualization technology to construct three types of RGB image which are utilized to train different classifiers, respectively, and a decision mechanism is designed to fuse the outputs of subclassifiers through weight analysis. This approach simultaneously extracts different types of features, which preserve application information comprehensively. Different classifiers are trained by these features to guarantee independence of each feature and classifier. On this basis, a comprehensive analysis of many methods is performed on the Android malware dataset, and the results show that our method has better efficiency and adaptability than others.

Trusted Platform Module (TPM) is a coprocessor for detecting platform integrity and attesting the integrity to the remote entity. There are two obstacles in the application of TPM: minimizing trusted computing base (TCB) for reducing risk of flaws in TCB, for which a number of convincing solutions have been developed; formal guarantees on each level of TCB, where the formal methods on analyzing the application level have not been well addressed. To the best of our knowledge, there is no general formal framework for developing the TPM-based protocol applications, which not only guarantees the security but also makes it easier for design. In this paper, we make fine-grained refinement on TPM-based security protocols to illustrate our formal solution on the application level by using the Event-B language. First, we modify the classical Dolev-Yao attacker model, which assumes normal entity's compliance with the protocol even without TPM's protection. Thus, the classical security protocols are vulnerable in this modified attacker model. Second, we make stepwise refinement of the security protocol by refining the protocol events and adding security constraints. From the fifth refinement, we make a case study to illustrate the entire refinement and further formally prove the key agreement protocol from DAAODV, the TPM-based routing protocol, under the extended Dolev-Yao attacker model. The refinement provides another way of formal modeling the TPM-based security protocols and a more fine-grained model to satisfy with the rigorous security requirement of applying TPM. Finally, we prove all the proof obligations generated by Rodin, an Eclipse-based IDE for Event-B, to ensure the soundness of our proposal.