Machine learning-based Android malware detection has consistently demonstrated superior results. However, with the continual evolution of the Android framework, the efficacy of the deployed models declines markedly. Existing solutions necessitate frequent and expensive model retraining to resist the constant evolution of malware accompanying Android framework updates. To address this, we introduce a solution called ASDroid, which generalizes specific APIs into similar API clusters to counteract evolving Android malware threats. One primary challenge lies in identifying analogous API clusters that correspond to specific APIs. Our approach involves extracting semantic information from open-source API source code to construct a heterogeneous information graph, and utilizing embedding algorithms to obtain semantic vector representations of APIs. APIs that are close in embedding distance are presumed to have similar semantics. Our dataset encompasses Android applications spanning nine years from 2011 to 2019. In comparison to existing Android malware detection model aging mitigation solutions like APIGraph, SDAC and MaMaDroid, ASDroid demonstrates greater accuracy and more effective at resisting continuously evolving malware.

The digital twin acquires the environment states through device data synchronization, providing robust support for improving collaboration efficiency in edge-end collaborative networks. Grouping is an effective way to reduce competition and simplify scheduling, with multiple devices sharing spectrum resources within the group through power control to synchronize data. However, unreasonable grouping and power control will lead to uneven load and frequent collisions. We propose a data synchronization optimization algorithm for the digital twin based on grouped load balance and mask-assisted power control to handle this problem. First, we allocate periodic devices to groups that maximize the least common multiple of the period in the group to reduce collisions. Event-triggered devices are allocated to groups based on balancing each group’s cumulative average packet generation rate. Second, we apply multi-agent reinforcement learning for power control of grant-free devices. Masks are used to shield prohibited actions to reduce the impact on grant-based devices, and agents can utilize auxiliary experiences to learn the impact of prohibited actions without interaction. Finally, we design some experiments, and the results show that our schemes can reduce collisions while balancing the load and perform better than other schemes after convergence.

Data sharing helps the digital twin builders obtain the necessary data to construct a virtual replica of the physical entity. The consortium blockchain is one of the effective ways to solve the lack of trust among these participants, but the traditional PBFT cannot distinguish abnormal nodes and has poor scalability. We propose an efficient PBFT algorithm based on comprehensive reputation and node grouping for data sharing in digital twins. First, we design a comprehensive reputation evaluation mechanism to distinguish normal and abnormal nodes according to their behavior in the processes of data sharing and consensus. Secondly, a node grouping algorithm is proposed, in which nodes with high reputation form a committee, while other nodes are randomly assigned to different groups. Finally, we design consensus processes separately for the group and committee. Each transaction is recorded in the consortium blockchain when the group and committee have reached a consensus on it in sequence. Experimental results show that the CRNG-PBFT algorithm can effectively identify abnormal nodes, reduce transaction delay, and improve throughput.