International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | A Privacy-Preserving IoMT Security Framework Integrating Transformer Autoencoder Anomaly Detection with Hyperledger Sawtooth Blockchain |
|---|---|
| ABSTRACT | The rapid proliferation of Internet of Medical Things devices, including wearable sensors, implantable monitors, and cloud-interfaced diagnostic systems, has introduced a critical and expanding attack surface alongside its transformative clinical benefits. IoMT environments are characterized by resource-constrained hardware, heterogeneous protocols, and highly sensitive patient data transmission, making them prime targets for botnet-driven distributed denial-of-service attacks, data exfiltration, and network intrusion. Existing security frameworks treat anomaly detection and audit logging as loosely coupled modules, lacking the latency guarantees, privacy protections, and regulatory alignment required for live hospital infrastructure. This paper proposes a tightly integrated, privacy-preserving security framework combining a Transformer-based Autoencoder for real-time anomaly detection with a Hyperledger Sawtooth permissioned blockchain for immutable audit logging. The Transformer Autoencoder is trained exclusively on benign traffic from the N-BaIoT dataset using an unsupervised reconstruction-loss paradigm, learning a compact latent representation of normal behavior that enables detection of deviations induced by sophisticated attack traffic. Upon detection, a middleware pipeline constructs a privacy-preserving metadata payload comprising a device identifier, timestamp, anomaly score, and SHA-256 cryptographic hash of the raw input, committing it to the distributed ledger without exposing any sensitive data on-chain. Empirical evaluation demonstrates 98.6% accuracy, 97.1% precision, and 98.4% recall on the N-BaIoT test set, outperforming eight contemporary baselines whilst maintaining 19ms inference latency. The blockchain layer sustains commit latency below 90 milliseconds at a two-kilobyte payload, with linear throughput scalability from 20 to 48 transactions per second. The off-chain hash-only storage model resolves the blockchain-GDPR paradox, satisfying both Article 17 right-to-erasure obligations and HIPAA transmission security standards through deliberate architectural design rather than post-hoc compliance measures. |
| TITLE | |
| AUTHOR | CHOUKPIN ADOTO MIGNONKOUN SOUROU YANNICK, HOJAMYRADOVA MEYREM, SALAMI IBRAHIM OLUSOLA MSc Student, Dept. of CST and Software, Nanjing University of Information Science and Technology, Nanjing, China MSc Student, Dept. of Information Science and Technology, Nanjing Forestry University, Nanjing, China BSc Student, Dept. of CST and Software, Nanjing University of Information Science and Technology, Nanjing, China |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405002 |
| pdf/2_A Privacy-Preserving IoMT Security Framework Integrating Transformer Autoencoder Anomaly Detection with Hyperledger.pdf | |
| KEYWORDS | |
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