2026· ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)· 0 citations
TL;DR
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.
Abstract
The increasing complexity of cyber threats across IoT-cloud infrastructures necessitates the use of innovative, flexible, and confidentiality-preserving prevention techniques. The Blockchain-Assisted Hybrid Attention-Based Intrusion Detection and Access Control System (BHA-IDACS) is presented in this paper. The primary detection module employs an Adaptive Spatio-Temporal Representation Architecture-Self-Attention and Intersample Attention Transformer (Astra-SAINT) to precisely detect evolving intrusion tendencies. A heron optimization algorithm (HOA) is utilized for tuning the model thereby improving accuracy of detection and convergence. Fully Homomorphic Encryption (FHE) maintains the security of data and storage of encrypted data in unsecured cloud and blockchain circumstances. On a Consortium Blockchain, all encrypted transactions and audit trails are maintained by a Proof-of-Stake Authority (PoSA) consensus method. Additionally, based on user behavior and trust level, Smart Contract-Based Dynamic Access Control independently enforces permission and authentication regulations. The suggested model provides better precision, recall, F1-score, F2-score, specificity, and Cohen's Kappa values in addition to a mean accuracy of 99.16%. Furthermore, statistical analysis using confidence intervals and low standard deviation values demonstrates that Astra-SAINT is reliable and consistent across all validation folds. These 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.
Secure and transparent system for recording and verifying digital transactions across distributed networks. Distributed blockchain consensus is achieved through decentralized protocol rules, cryptographic authentication mechanisms, and scalable energy-efficient operations. The present study applies Quantum Mayfly Optimization (QMFO) within a blockchain-based collaborative intrusion detection framework. Collaborative intrusion detection systems (CIDS) have certainly carved their valued place in enhancing modern cybersecurity in the complex landscape of cyber threats. What the BCIDF brings into the picture is a new radical avenue to enhance the detection of new threats and information sharing. In this respect, the proposal cohesively combines distributed blockchain technology and collaborative intrusion detection to increase security, transparency, and trust within cyber realms. Fine-tuning the model parameters will improve blockchain classification accuracy and efficiency, and O(QMFO), a bio-inspired hybrid algorithm inspired by the principles of quantum leaf-edge swarm behavior, is directed toward ensuring the security and performance of blockchain networks. Quantum Mayfly optimization (QMFO) and a Blockchain-based Collaborative Intrusion Detection Framework (BCIDF) are designed to secure distributed networks by allowing tamper-resistant sharing of alerts in the case of an attack across the blockchain. The term 'Quantum Mayfly Optimizer (QMFO)' here is used to amplify performance, speed, and accuracy. Integration, therefore, guarantees the best detection and few false positives, and ensures adaptive actions against upcoming threats.
M. Savitha, I. P. Stella Mary, A.Manikandan et al.· 2026 6th International Confe...· 0 citations
The study proposes a secure and adaptive intrusion detection model using Federated Learning and Blockchain, augmented with autoencoder-based feature reduction, showing that combining FL, blockchain, and deep feature extraction offers a viable and secure solution for intrusion detection systems in IoT.
Tahseen A. Wotaifi· Journal of Intelligent Infor...· 0 citations
Cloud storage systems have turned out to be a mandatory factor in handling and storage of high amounts of sensitive data in contemporary online surroundings. Nevertheless, they are becoming more vulnerable to high risks in security matters like information breaches, unauthorized access, insider threats, and advanced cyberattacks. The security mechanisms that have been used in the past are not always adequate to offer all-time protection against these emerging threats. In order to overcome these shortcomings, this paper offers a secure cloud storage architecture incorporating Blockchain technology and threat detection with Artificial Intelligence (AI) technology. To guarantee the integrity of the data in a decentralized and tamper-resistant manner, blockchain provides a secure storage of file hashes and the records of transactions, which cannot be subjected to any alterations. At the same time, a threat detection engine based on AI will constantly keep track of user actions and access patterns to detect anomalies and malicious actions in real-time. The hybrid solution proposed improves the security, transparency, and reliability of data as well as allows proactive mitigation of threats. Experimental analysis has shown that Blockchain and AI integration can considerably enhance the overall security posture of cloud storage systems with regard to traditional approaches.
Sivabharathi S, V. M, Thirisha B et al.· 2026 7th International Confe...· 0 citations
In the digital era, the prevalence of cyber threats within cloud-based infrastructures presents a formidable challenge. This study introduces a novel approach that combines the immutable nature of blockchain technology with advanced detection mechanisms to enhance the security of cloud environments. We propose a model that leverages the synergy of blockchain's distributed ledger capabilities and cutting-edge intrusion detection systems (IDS) to establish a dynamic and decentralized framework for cyber-attack detection and prevention. Our innovative method involves a multi-layered detection algorithm that operates in conjunction with a blockchain network to make sure the data integrity and veracity of application transmissions. With integration, the proposed system not only detects but also systematically records cyber attack patterns, thereby creating a robust database of digital signatures that can be used for future prevention measures. This proactive approach ensures a swift and secure method of identifying potential threats, which will significantly reduce the risk of data breaches along with system infiltrations. The implementation of this method is anticipated to provide a reliable and transparent mechanism for safeguarding sensitive information stored within cloud services. It advances cybersecurity, protecting service providers and end-users from changing cyber threats.
Eruguralla SatishBabu, Smitha Chowdary· International Conference Com...· 0 citations
Results indicate that decentralized, interoperable, and energy-aware intrusion detection is feasible for large-scale IoT deployments, particularly in resource-constrained IoT environments.
S. Bassey, Emmanuel Udoh, B. Stephen et al.· E3S Web of Conferences· 0 citations
The proposed hybrid approach outperforms ML-only and blockchain-only baselines, offering a scalable, secure, and real-time IDS for IIoT infrastructures.