The cybersecurity threats targeting industrial control systems (ICS) are evolving with increasing sophistication. Addressing the detection blind spots in existing source code analysis techniques, this study reveals a dual security paradox arising from code sensitivity: privacy leakage risks caused by decompilation techniques and integrity verification deficiencies in reverse engineering. This paper investigates three critical challenges: (1) What are the component flow process and detection elements of ICS component source code? (2) How can high-performance and reliable tracing and traceability be provided for ICS component source code exceptions and routine detection? (3) How can privacy enhancement and trusted detection of ICS component source code with high sensitivity be achieved? This paper proposes a blockchain-integrated trusted detection framework for ICS (BCTD-ICS), delivering groundbreaking solutions: (1) Establishing a lifecycle circulation model that systematically maps component types, stakeholders, and detection parameters; (2) Developing a tripartite collaborative architecture (Blockchain-Identification Resolution Zero-knowledge proofs (ZKPs)), featuring a traceability mechanism with trusted identification codes (resolution efficiency: 40ms/105 queries) to eliminate decompilation-induced privacy risks; (3) Creating an industrial-oriented privacy enhancement system utilizing DBSCAN clustering for intelligent sampling (26% compression rate on BCN3D Moveo) and optimizing ZK-SNARK protocols through Shamir’s Secret Sharing, establishing a backdoor-resistant distributed parameter generation system (time delay increment < 100ms). Experimentally verified, our solution enables ICS component code detection supply-chain-wise without sensitive data leakage in real-world industries. This work establishes a novel trusted detection paradigm for ICS, advancing detection efficiency and credibility under strict privacy preservation requirements, meeting Industry 4.0 security demands.

Telecommunications fraud incidents have occurred frequently around the world. Rapid and accurate detection of fraudsters in telecommunication networks has become a hot topic for researchers. Due to data imbalance and dependency on labels, many Graph Neural Networks (GNNs)-based methods cannot achieve good representation ability in Telecommunications Fraud Detection (Telecom-FD) tasks. Recently, Graph Contrast Learning (GCL) has emerged as a successful method for fraud detection due to its unsupervised graph representation learning ability. However, existing GCL-based methods typically adopt uniform strategies to drop edges and impose perturbations on features during the data augmentation process, which tends to eliminate certain influential edges, consequently reducing the quality of the embeddings. To overcome the above problems, a novel framework named TFD-GCL is proposed to adaptively generate contrastive instances for effective Telecom-FD. Specifically, encoding anchor nodes and generating positive instances by automatically learning from neighbors using the attention mechanism. Then, the end-to-end adaptive learning global average similarity is devised to evaluate interaction information between neighbors and construct negative instances from dissimilar neighbors. In addition, TFD-GCL uses a joint contrastive loss to improve the representational capability of fraudulent nodes in unbalanced data. Extensive experiments on the real-world dataset demonstrate that TFD-GCL has better performance compared with classical Telecom-FD methods when the labels are extremely limited.

The Internet of Medical Things (IoMT) plays a crucial role in advancing smart healthcare by facilitating the real-time collection and processing of medical data. These interconnected devices leverage Artificial Intelligence to assist practitioners in making data-driven decisions. However, IoMT’s dependence on communication protocols exposes it to significant security vulnerabilities. In response to this challenge, we propose a novel Meta-Intrusion Detection System (Meta-IDS) that employs a meta-learning approach to enhance the detection of both known and zero-day intrusions. Our approach seamlessly integrates signature-based and anomaly-based detection techniques, incorporating privacy-preserving methods essential for handling sensitive IoMT data. We rigorously evaluated our methodology using three publicly available datasets (WUSTL-EHMS-2020, IoTID20, and WUSTL-IIOT-2021). The results demonstrate remarkable accuracy rates of 99.57%, 99.93%, and 99.99% for signature-based detection, and 99.47%, 99.98%, and 99.99% for anomaly-based detection, coupled with impressively low misclassification rates of 0.0042%, 0.0006%, and 0.00004%, respectively. Through a comparative analysis with the state-of-the-art E-GraphSAGE model, considering metrics such as accuracy, precision, recall, F1-score, time complexity, and misclassification rate, we affirm the performance and reliability of the Meta-IDS. Our approach holds significant promise in bolstering cybersecurity within the IoMT network.