Jul 2026· International Conference Computing Methodologies and Communication· pp. 102-110· 0 citations· 14 references
Abstract
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.
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
Smart cities (SCs) use the Internet of Things (IoT) to collect and process data to communicate with their infrastructure and assets in real time. A great deal of techniques, such as encryption protocols, Random Forest-based AI-driven threat detection, and blockchain architectures, have been developed to address cybersecurity challenges in smart cities (SCs). These techniques, however, have limitations such as their scalability, high computational expenses, and energy inefficiency. Therefore, in this study, to overcome these challenges, we propose a blockchain-based infrastructure called BlockSafeNet. This uses artificial intelligence, big data, and blockchain to enhance cybersecurity in SCs. The effectiveness of the proposed BlockSafeNet framework was evaluated using responsiveness, computational time, encryption quality score, detection rate, false positive rate, latency, throughput, and energy consumption as the primary cybersecurity performance metrics. These metrics were selected to assess communication efficiency, threat detection capability, privacy preservation, scalability, and overall security performance within smart-city IoT environments. To ensure secure data transactions, robust threat detection, and efficient communication. The system’s high calculation speed and detection rate show potential for managing sensitive maternal health data collected by IoT devices. The platform also shows how IoT may be used by healthcare services to monitor public health in real time, allowing hospitals, emergency services, and public health agencies to securely share data. This aids in resource optimization, improving service delivery, and preserving data privacy and trust in SCs. Data was obtained from the UCI Machine Learning Repository on Kaggle to validate the developed framework. By evaluating the effectiveness of BlockSafeNet in tackling cybersecurity challenges, we establish its practical relevance and usability in SCs. The proposed BlockSafeNet framework achieved a responsiveness of 24 s, an encryption quality score of 0.89, computational time of 85 s, and a detection rate of 91%, demonstrating significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures. shows that SC IoT security has significantly improved through the adoption of new data protection methods and better measures of security, providing a positive impact on the SC ecosystem.
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
An end‐to‐end IoT‐cloud security system that is based on markov decision processes, reinforcement learning, and blockchain‐enhanced authentication in order to achieve better attack detection, false alarms, and safe device management is created.
Mohamed Loey, V. Krishna, Osama S. Younes et al.· Transactions on Emerging Tel...· 0 citations
As the new applications like smart cities, medical systems, industrial automation, and critical infrastructures, along with the Internet of Things (IoT) technologies, have emerged, the need for real-time data processing at the edge has also grown. Although the edge-based IoT architectures minimize the latency and bandwidth consumption, they also present new security problems because of the distributed implementation, limited computational power, and lack of centralized control. The traditional, cloud-based, and black box artificial intelligence (AI) security system simply does not fit such a context without being transparent, responsible, and sustainable. Furthermore, in the real world, if IoT applications are designated to be safe, they cannot be realized by using black-box decision models without trust, compliance with regulations, and reliability through time. The paper proposes an edge-based IoT security framework to improve the cyber resilience and sustainability and ethical decision-making through the interpretable and responsible AI models. The proposed solution will be based on the use of explainable and light algorithms of machine learning at the network edge to monitor malicious actions and provide friendly explanations of the security actions to a human. It supports interpretability, fairness, accountability, and detection accuracy to detect threats and respond appropriately in dynamic environments of IoT, making the system accountable, fair, and sensible. The study with realistic IoT intrusion data sets proves that the proposed approach is competitive at detection and is more transparent and stable regarding various traffic conditions. The results show the importance of developing AI models that are interpretable and responsible for sustainable, credible, and resilient edge-based IoT security systems.
M. Kathiravan, M. Manikandan, R. Buvanesvari et al.· 2026 International Conferenc...· 0 citations
The integration of IoT into smart cities exposes serious cyber security risks, which old centralized designs would fail to combat. In this paper, the model suggests a unified system that would use AI and block chain technology to protect smart city IoT systems. A lightweight hybrid CNN-LSTM model is used to detect anomalies in real-time at the edge nodes and federated learning with differential privacy can be used to train collaboratively without exposing raw data. Permissioned block chain layer provides tamper-proof records and decentralized management of trust. A query fragment caching algorithm is a resource-optimal query strategy to block chain queries. CIC-IDS2017 and BoT-IoT datasets analysis show 98.6% detection, 97.5% F1-score, 134.6 ms latency, and 2,615 transactions-per-second, and 41% less energy usage than un cached block chain access. The framework is more effective than the current methods in all measures.