While deep learning techniques have been extensively employed in malware detection, there is a notable challenge in effectively embedding malware features. Current neural network methods primarily capture superficial characteristics, lacking in-depth semantic exploration of functions and failing to preserve structural information at the file level. Motivated by the aforementioned challenges, this paper introduces MalHAPGNN, a novel framework for malware detection that leverages a hierarchical attention pooling graph neural network based on enhanced call graphs. Firstly, to ensure semantic richness, a Bidirectional Encoder Representations from Transformers-based (BERT) attribute-enhanced function embedding method is proposed for the extraction of node attributes in the function call graph. Subsequently, this work designs a hierarchical graph neural network that integrates attention mechanisms and pooling operations, complemented by function node sampling and structural learning strategies. This framework delivers a comprehensive profile of malicious code across semantic, syntactic, and structural dimensions. Extensive experiments conducted on the Kaggle and VirusShare datasets have demonstrated that the proposed framework outperforms other graph neural network (GNN)-based malware detection methods.

Cloud platforms provide extendable and flexible orchestration capabilities for microservices by consolidating multiple computing resources, e.g., multiple service instances can be deployed redundantly to form server clusters, so that if a cloud node or service instance fails unexpectedly, a copy of the service on another node can take over the failed instance to ensure persistent reliability of the system. However, such redundant deployments in cloud may result in unbalanced utilisation of resources, which complicates software quality assessment on cloud nodes. Moreover, the maximisation of the quality of service (QoS) is recognised as a NP problem, making cloud services difficult to be optimised in real-time. To solve these problems, this paper proposes a scheduling combination method for microservices on cloud platforms based on a dual-layer QoS evaluation model, which renders the dual superposition effect of cloud nodes and service instances on system quality. The advantage of the model is that it considers the coupling relationship between cloud hardware or software quality. Further, to optimise QoS in cloud environments based on the model, a hybrid Vision-improved Ant Colony-Genetic algorithm called VACG is proposed to solve the combination optimisation problem and find optimum composition solutions. Our experimental results demonstrate that the scheduling policy based on the dual-layer QoS model surpasses another non-scheduling policy on the metrics of system QoS index and service level agreement conflicts. Additionally, it is found that VACG has a 17.13% and 27.03% of improvement on the optimisation accuracy over a genetic algorithm and ant colony optimisation respectively, as well as higher computational efficiency and stability.