Data outsourcing improves the scalability of large-scale storage systems but introduces risks related to data tampering and unverifiable ownership claims. Existing provable data possession (PDP) schemes often incur substantial cryptographic overhead and primarily focus on data integrity. This paper proposes a lightweight PDP scheme that uses a user-specific polynomial to construct integrity tags without bilinear pairing operations. The scheme is integrated with a blockchain smart contract to provide dual verification: The file owner verifies outsourced data integrity, while the smart contract validates ownership proofs, challenge freshness, file versions, and record validity on behalf of the external server. Experimental results identify πd=3 and Q=60 as practical parameter settings. The implemented protocol completes recurring setup, ownership registration, and ownership verification in 465.95, 27.47, and 54.33 ms, respectively. Under a common primitive operation model, the proposed scheme achieves the lowest tag generation cost and a lower integrity audit cost than five of the six compared schemes. The results demonstrate efficient and scalable integrity and ownership verification for outsourced storage systems.
The escalating threat of ransomware in Industrial Internet of Things (IIoT) networks necessitates advanced detection methods to protect critical infrastructure. This article proposes a robust ransomware detection and mitigation framework that integrates generative adversarial network (GAN)-based data augmentation with machine learning (ML) classifiers within a software-defined networking (SDN) environment. To address severe class imbalance in IIoT traffic, synthetic ransomware samples are generated to augment the EdgeIIoTset dataset. This enhanced dataset trains multiple ML classifiers—including decision tree (DT), light gradient boosting machine (LightGBM), eXtreme gradient boosting (XGBoost), and random forest (RF)—leveraging dynamic feature selection to identify critical network attributes. Experimental evaluations demonstrate that GAN-enhanced training enables the XGBoost classifier to achieve 99.92% accuracy, 100% precision, 99.26% recall, and a 99.63% F1-score, alongside a low false positive rate of 3.73×10−6. This represents a 4.5% improvement in recall compared to nonaugmented baseline models. Furthermore, an adaptive mitigation threshold of 97% malicious packets effectively blocks attack flows in real time, reducing attack throughput to zero. These figures of merit confirm the efficacy and scalability of coupling generative data balancing with programmable SDN control for IIoT security.
The rapid increase in internet usage has led to a surge in network threats, making traditional signature-based intrusion detection systems (IDS) less effective. This paper presents a machine learning-based, open-source framework designed for real-time detection of malicious network traffic. The proposed system uses a flowmeter to extract time and size-based features from network traffic, followed by a preprocessing pipeline that enhances detection accuracy. After evaluating various models, the deep belief network (DBN) was selected for its superior performance, achieving a weighted F1 score of 99.83 and a prediction time of 2.57 s. To improve scalability and efficiency, the framework integrates with the Wazuh security information and event management (SIEM) system, enabling real-time alerts and flexible deployment across networks. Simulations conducted using graphical network simulator-3 (GNS3) confirm the framework’s effectiveness in real-time intrusion detection.
Private transfers on public smart contract blockchains hide transaction values and private transfer links, but accountable privacy requires controlled auditability. Existing auditable zero-knowledge transfer systems make per-transaction audit information available to an authorized auditor, but their audit model may allow the auditor to inspect transactions beyond the flow connected to the authorized audit target. In such a model, granting audit capability for one investigation can expose unrelated transactions or allow tracing to continue farther than intended. In this paper, we propose token-guided flow tracing for auditable zero-knowledge smart contract transfers, enabling audit authorization to be scoped to a transaction flow and epoch range. The construction separates audit authorization from audit capability by introducing a tracing-token manager and an auditor. The auditor can open audit information only when it holds the auditor secret key, an epoch secret key for an authorized epoch, and a tracing token associated with the target transaction flow. Direction-specific tracing tokens confine tracing to the authorized direction, while epoch public keys and a binary key-derivation tree support compact authorization of audit intervals. We analyze security through ledger indistinguishability, transaction nonmalleability, balance, audit correctness, and restricted audit authorization. Restricted audit authorization is established in an honest authorization model that assumes the tracing-token manager and the auditor do not collude beyond explicit audit authorizations; under these assumptions, the auditor cannot open or trace transactions outside the authorized flow, direction, and epoch scope. Our implementation adds 16,349 transfer-circuit constraints, about 178,000 gas to each accepted transfer, and 448 bytes to the serialized transfer transaction, showing that scoped auditability can be added with moderate on-chain overhead.
Machine‐type communication (MTC) is a fundamental enabler of the Internet of Things (IoT) and emerging 5G/6G networks, supporting massive deployments of heterogeneous and resource‐constrained devices. However, large‐scale data collection, persistent connectivity, and limited device capabilities introduce critical privacy challenges that are not adequately addressed by conventional security mechanisms designed for human‐centric or homogeneous networks. This survey presents a comprehensive analysis of privacy threats and countermeasures in MTC networks. Using a domain‐based approach aligned with the ETSI M2M architecture, we examine privacy vulnerabilities and attacks across the device, network, and application domains. We further provide a structured classification of privacy‐preserving solutions, encompassing identity protection, data confidentiality, and behavioral obfuscation, and compare them in terms of effectiveness and deployment feasibility. Finally, we identify open challenges and research directions for scalable and lightweight privacy protection in massive MTC environments and propose a cybersecurity framework that integrates technical, regulatory, and operational considerations to support trustworthy MTC systems.
