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INTERNATIONAL JOURNAL OF RECENT TRENDS IN TECHNOLOGY AND ENGINEERING (IJRTTE)

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Blockchain-Verified Federated Intrusion Detection Models

Y Prasanna Kumar
Professor, School of Mining Engineering, Faculty of Engineering, PNG University of Technology, India.
Murali Muthusamy
Managing Director Cum Research Analyst, Aryaa Infostat Technologies, India.

Keywords: Blockchain; Federated Learning; Intrusion Detection System; IoT Security; Model Verification; PBFT Consensus; Edge Computing; Cybersecurity; Distributed Learning; Privacy-Preserving IDS

Abstract

Internet of Things (IoT) devices developed so fast that nowadays cyber-attacksbecome more frequent, and Intrusion Detection Systems (IDS) are the key to protecting a modern network. The traditional centralized IDS solutions however have some significant disadvantages like privacy, it is also expensive to communicate and also prone to failure at one point. Another option is Federated Learning (FL) that allows local model training without sharing the raw data, yet it is still vulnerable to such threats as model poisoning and untrusted updates. As a way of dealing with these challenges, this paper suggests a Blockchain-Verified Federated Intrusion Detection Model (BV-FLIDM), which integrates the privacy advantage of FL with the integrity and transparency of blockchain. The proposed system assumes all model updates created by IoT devices will be validated with a lightweight PBFT-based blockchain layer, which will be followed by federated aggregation to have tamper-proof and reliable learning. Tests on NSL-KDD, CIC-IDS-2018, and Bot-IoT datasets indicate that BVFLIDM has a higher detection, lower communication, higher resistance to adversarial attacks, and better scalability relative to current FL-only and blockchain-only IDS solutions. These results indicate that BV-FLIDM is a powerful and convincing way of safeguarding the heterogeneous IoT settings.
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Published
2025-09-21
Pages
1-12
Issue
Vol. 4 No. 3 (2025): IJRTTE - 04 - 03
Section
Articles

References

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