An Efficient Malware Detection Framework for Enhancing Software Security in Resource-Constrained Systems

The increasing adoption of Industrial Internet of Things (IIoT) networks has introduced new security challenges, particularly to ensure software security against evolving malware threats. IIoT systems rely on interconnected edge, cloud, and embedded devices, which are highly vulnerable to malware attacks that exploit software vulnerabilities, propagate across networks, and compromise industrial operations. However, existing malware detection approaches often struggle with resource constraints, high-dimensional feature spaces, and the need for real-time adaptability, making them inefficient for large-scale IIoT deployments. To address these challenges, this paper presents "GWPSO-GAMD," a resource-efficient malware detection framework designed to enhance software security in IIoT networks. The framework integrates a hybrid metaheuristic feature selection algorithm—Grey Wolf Optimization and Particle Swarm Optimization (GWPSO)—to identify the most discriminative and computationally efficient features, reducing processing overhead while maintaining high detection accuracy. These features are then analyzed by the Graph Android Malware Detector (GAMD), which leverages graph convolutional networks (GCNs) and attention mechanisms to model malware propagation behaviors and uncover complex attack patterns in IIoT environments. Empirical evaluations on two open-source malware datasets, CIC-MalDroid-2020 and CIC-MalMem-2022, demonstrate that the proposed model achieves detection accuracy above 98.41% while significantly reducing CPU usage by 47%, memory footprint by 57%, and training time by 67%. The results highlight GWPSO-GAMD’s ability to provide scalable, real-time malware detection for resource-constrained IIoT systems, advancing the vision of secure, intelligent, and resource-aware IIoT networks.