B-Prim: A Multidisciplinary Framework for IoT–Cloud Ecosystems Privacy – Preserving Real-Time Intrusion Detection and Isolation

DOI

10.2991/978-94-6239-789-7_23How to use a DOI?

Keywords

Blockchain security; Internet of Things; edge computing; local differential privacy; intrusion detection; privacy-preserving machine learning; and Byzantine resilience

Abstract

The worldwide network perimeter is now a pathwork of exploitable gaps due to billions of unprotected IoT devices. Current intrusion detection systems are confronted with a recurring also breaches the privacy regulations that contemporary deployments are required by law. This study presents B-PriM, a decentralized IoT security architecture that provides autonomous node isolation and real-time intrusion detection without ever disclosing raw traffic. B-PriM by combining a blockchain–backed decentralized reputation score system with a lightweight Edge-GRU with local differential privacy. We prove (epsilon delta) –differential privacy for each communication round and formalize detection- isolation as a combined optimization over privacy loss and inference delay. F1 of 95.8% +/- 0.5% at operating configuration ε = 1.0(δ = 1e-5), sub- 3.8 ms latency, and 91.0% accuracy at 30% Byzantine fraction are obtained from experiments on the Edge-IIoTset and CICIoT2023 Datasets, which significantly exceed four federated baselines (P< 0.01).

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rajul Ravi
AU  - Sandeep Kumar
AU  - Aashutosh K. A. Prashant
PY  - 2026
DA  - 2026/10/08
TI  - B-Prim: A Multidisciplinary Framework for IoT–Cloud Ecosystems Privacy – Preserving Real-Time Intrusion Detection and Isolation
BT  - Proceedings of theInternational Conference on Innovation and Practices in Artificial Intelligence (ICIPAI 2026)
PB  - Atlantis Press
SP  - 315
EP  - 331
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-789-7_23
DO  - 10.2991/978-94-6239-789-7_23
ID  - Ravi2026
ER  -