Aug 2026· Italian National Conference on Sensors· Vol 26· 0 citations· 26 references
Medicine
TL;DR
A Weighted Symmetric Hashed Blockchain framework that integrates mutual-information-based feature weighting, deep-learning-based attack classification, AES-256-GCM authenticated encryption, cryptographic hashing, and permissioned-ledger logging, demonstrating the potential of WSHB as a reproducible framework for attack classification and secure event logging in cloud-integrated sensor environments.
Abstract
Cloud-integrated sensor and Internet of Things (IoT) systems enable scalable data storage, processing, and intelligent monitoring, but their distributed nature exposes network-flow and host-level data to unauthorized access, tampering, and cyberattacks. This study proposes a Weighted Symmetric Hashed Blockchain (WSHB) framework that integrates mutual-information-based feature weighting, deep-learning-based attack classification, AES-256-GCM authenticated encryption, cryptographic hashing, and permissioned-ledger logging. The framework was evaluated independently using HIKARI-2021 for network-based intrusion detection and ADFA-LD for host-based intrusion detection. A transparent comparative evaluation was conducted against LSTM and CNN–RNN baselines using identical data splits, preprocessing settings, input representations, hyperparameter-search budget, and repeated initialization seeds. The proposed WSHB-DNN classifier achieved macro-F1 scores of 0.6837 on HIKARI-2021 and 0.7914 on ADFA-LD, showing the strongest overall classification performance among the evaluated models. The cryptographic and permissioned-ledger components provide confidentiality protection, record-level authentication, integrity verification, and tamper-evident logging for confirmed attack-event records. These results demonstrate the potential of WSHB as a reproducible framework for attack classification and secure event logging in cloud-integrated sensor environments.
BELS-IoT is proposed, a novel decentralized protection architecture that integrates a cryptocurrency-based blockchain layer with a multi-layer ensemble learning engine that rewards honest behavior and penalizes malicious activities while maintaining privacy through federated learning with blockchain-verified reputation scores.
Anwar Kalghoum, Leila Azouz Saidane· SN Computer Science· 0 citations
Internet of Things (IoT) devices are increasingly targeted by rapidly evolving malware, yet collaborative detection remains challenged by privacy leakage, noisy and imbalanced training data, and weak integrity guarantees when sharing model updates. This paper presents Mal-Fedchain, a secure and privacy-preserving framework for image-based IoT malware detection and prevention that couples federated learning with blockchain and honeypot-assisted behavioral monitoring, targeting Linux-capable IoT gateway devices. Portable Executable (PE) binaries are transformed into grayscale images using a corrected fixed-width byte-mapping pipeline stabilized by an information-maximizing GAN (IMGAN). A bi-level preprocessing pipeline applies two-sided weighted sparse representation (T-WSR) denoising—designed to selectively suppress zero-padding artifacts, high-entropy packed regions, and sparse opcode noise while preserving discriminative section-boundary texture—followed by geometric augmentation to mitigate class imbalance. Malware detection and family attribution are performed using a residual capsule-based network (RBCN) that fuses discriminative visual representations with PE-header features via concatenation, improving robustness against polymorphism and obfuscation. A formal threat model governs three adversary classes: a semi-honest aggregation server, a bounded fraction of malicious clients (up to 30%), and a passive eavesdropper. To enable collaboration without exposing raw data, clients train locally and share only MemCbar-encrypted updates; a permissioned Hyperledger Fabric blockchain ledger records hashed updates and security events to provide integrity, traceability, and tamper resistance. A file-system-integrated honeypot captures evasive behaviors and logs auditable evidence to strengthen prevention. Experiments on the Malimg dataset across five ablation configurations demonstrate that the corrected RBCN pipeline achieves 93.52% accuracy, 92.40% precision, 93.52% recall, 92.52% F-measure, MCC of 0.9245, and AUC of 0.9976 in its centralized configuration, and 65.62% accuracy with AUC of 0.9840 in the full federated configuration with five clients and eight communication rounds, substantially outperforming all baselines across all reported metrics.
Najem N. Sirhan, Riyad Alrousan, H. Fakhouri· IoT· 0 citations
—The proposed study suggests a to help cope with issues related to cybersecurity in Internet of Things and Industrial Internet of Things environments without compromising privacy. The proposed framework introduces several innovative features, such as federated learning with momentum-based optimization, adaptive differential privacy, trust verification via blockchain, and Byzantine-resilient aggregation, to enhance the security, scalability, and robustness of the system compared with traditional intrusion detection systems. It also integrates supervised classification with autoencoder-based anomaly detection to detect existing and emerging cyberattacks. The proposed system was assessed with respect to the extended Industrial Internet of Things Intrusion Dataset (X-IIoT) and Network-Based Botnet Attack detection for IoT (N-BaIoT) benchmark datasets, where the environments were simulated as federated ones. The accuracy of the Hybrid Robust Federated Intrusion Detection System increased to 97.15% on X-IIoT and 97.64% on N-BaIoT with only 41 communication rounds and was resilient against up to 20% of Byzantine clients. These results showcase its efficacy to secure, private and communication-efficient intrusion detection for next generation Internet of Things and Hybrid Robust Federated Intrusion Detection System networks.
The study proposes a secure and adaptive intrusion detection model using Federated Learning and Blockchain, augmented with autoencoder-based feature reduction, showing that combining FL, blockchain, and deep feature extraction offers a viable and secure solution for intrusion detection systems in IoT.
Tahseen A. Wotaifi· Journal of Intelligent Infor...· 0 citations
The proposed framework effectively integrates encryption, federated intrusion detection, explainable artificial intelligence, and blockchain security to enhance privacy, transparency, and reliability in IoMT healthcare networks.
P. Banupriya, K. Vanitha· Journal of Vibration Enginee...· 0 citations
The proposed hybrid approach outperforms ML-only and blockchain-only baselines, offering a scalable, secure, and real-time IDS for IIoT infrastructures.