2026· E3S Web of Conferences· 0 citations· 7 references
TL;DR
Results indicate that decentralized, interoperable, and energy-aware intrusion detection is feasible for large-scale IoT deployments, particularly in resource-constrained IoT environments.
Abstract
The integration of Internet of Things (IoT) systems with blockchain-based security mechanisms offers improved trust, auditability, and decentralization, but introduces significant challenges related to interoperability, transaction latency, and energy consumption at the network edge. Most existing blockchain-assisted intrusion detection systems (IDS) are limited to single-ledger deployments and rely on computationally intensive machine-learning inference, which restricts their applicability in resource-constrained IoT environments. Presented in the work is a cross-chain and energy-efficient intrusion detection framework that combines lightweight machine-learning–based anomaly detection with smart-contract–driven verification and interoperable blockchain communication. Intrusion detection models are optimized using pruning and INT8 quantization to reduce inference overhead on IoT gateways, while verified intrusion alerts are securely propagated across heterogeneous blockchain platforms through cross-chain messaging protocols. Smart contracts automate alert validation and response actions, enabling coordinated defense across multiple ledgers. Experimental evaluation using CICIDS2017 and BoT-IoT datasets, physical IoT gateway hardware, and a multi-chain blockchain testbed demonstrates that the proposed framework achieves a detection accuracy of 97.4%, reduces inference energy consumption by up to 56%, and improves alert propagation latency by 42% compared to single-chain baselines. These results indicate that decentralized, interoperable, and energy-aware intrusion detection is feasible for large-scale IoT deployments.
The convergence of the Internet of Things (IoT) with Web3 ecosystems introduces new opportunities for automation, trust, and decentralized coordination. However, the same decentralized nature also amplifies security vulnerabilities, as conventional centralized intrusion detection and response systems are unable to provide real-time, tamper-proof protection at scale. This paper presents the Smart Contract-Based Automated Response System (SC-ARS), a novel framework that integrates blockchain smart contracts, machine learning (ML)-based anomaly detection, and automated mitigation policies. SC-ARS leverages lightweight consensus mechanisms and decentralized storage to ensure resilience against single points of failure, while smart contracts provide transparent and auditable enforcement of security actions. The ML pipeline, implemented using Random Forest, XGBoost, and LSTM models, is trained on benchmark datasets (NSL-KDD and CICIDS2017) to enable accurate anomaly detection. Experimental evaluation demonstrates up to 95% detection accuracy, a 50% reduction in response latency, and scalability to over 100,000 IoT devices without performance degradation. These results highlight the suitability of SC-ARS for deployment in smart cities, industrial IoT, and decentralized critical infrastructures where trust, transparency, and real-time responsiveness are essential.
S. Bassey, B. Stephen, Emediong Bassey Obot et al.· E3S Web of Conferences· 0 citations
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
This WSNs coupled with the Internet of Things (IoT) is very much needed in facilitating smart environment like smart cities, industrial automation, healthcare monitoring and environmental monitoring. Nevertheless, WSN-IoT systems are extremely susceptible to security risks such as denial-of-service attacks, malicious node behaviors, manipulation of routing, data manipulation, and privacy breaches due to their distributed, heterogeneous, and resource-constrained nature. Conventional centralized security models are usually insufficient to deal with these dynamic and scale cyber threats. The paper describes a detailed overview of blockchain-based and artificial intelligence (AI)-based security solutions to the distributed WSN-IoT scenarios. The paper examines the security dangers at varying layers of a IoT architecture and surveys the recent frameworks that incorporate machine learning, deep learning, and federated learning along with blockchain technology in managing decentralized trust and identify intrusions. Moreover, performance trade-offs on the effectiveness of security, energy use, latency, and scalability are discussed. Lastly, the research indicates the presence of an open challenge and future research topics of creating secure, scalable, and intelligent WSN-IoT infrastructure. The paper also suggests a solution of integrative AI-blockchain security framework that would have a combination of AI-based intrusion detection and decentralized blockchain trust management.
S. Madhuri, D. Dhevi· International Conference Com...· 0 citations
A Blockchain-Based IoT Security Architecture that integrates distributed ledger technology, smart contracts, edge computing, and zero-trust authentication mechanisms to enhance security, privacy, and system reliability is proposed.
K. Venkatesh, Gorre Bharath, Jannu Subhas Chandra Boss· International Scientific Jou...· 0 citations
The growth of the Internet of Things (IoT) has brought about security concerns owing to the massive global integration of diverse and constrained devices. Blockchain, Machine Learning (ML) and Deep Learning (DL) techniques have been proposed as effective ways to improve IoT security. This paper reviews the literature on the use of blockchain and smart learning techniques for security enhancement and threat detection in IoT networks. Blockchain offers a decentralized and immutable approach to secure data integrity, authenticating and controlling access to IoT devices, while ML and DL techniques allow intelligent monitoring of network data to detect anomalies and predict cyber-attacks. This study follows a systematic literature review approach, examining peer-reviewed journal articles and conference proceedings from 2019 to 2024. The analysis shows while blockchain, ML and DL technologies play a crucial role in enhancing IoT security, they each have limitations including scalability, computational overhead, data dependency and lack of flexibility against new cyber threats. Additionally, the majority of studies concentrate on either blockchain-based security or ML-based intrusion detection, with limited study on the integration of the two for real-time threat detection and mitigation. The study highlights this limitation and calls for the development of intelligent hybrid models that integrate blockchain technology with ML/DL to address scalability, adaptability and real-time security for IoT networks to ensure confidentiality, integrity and reliability.
Shamsudeen Mohammed S, Nwobodo-Nzeribe Nnenna Harmony, Aghaizu Herman Chijioke· International journal of re...· 0 citations
The BHA-IDACS results demonstrate the efficacy of the suggested Astra-SAINT framework as a scalable and dependable intrusion detection method for protecting IoT environments of the next decade.
C. Ramya, A. Suphalakshmi· ITEGAM- Journal of Engineeri...· 0 citations