Blockchain-Driven Transformer-Based Intrusion Detection and Access Control System (BDL-IDACS)

A Blockchain-based Collaborative Intrusion Detection Framework with Quantum Mayfly Optimization for Secure and Intelligent Threat Detection

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.

A Blockchain-Integrated Federated Learning Model and Autoencoder-Based Feature Reduction for Improving IoT Intrusion Detection

The rapid growth of Internet of Things (IoT) environments has brought forth a wealth of security challenges in detecting network intrusions in diverse and resource-restricted systems. Privacy, scalability, and single point of failure issues plague traditional centralized intrusion detection solutions. To address these challenges, the study proposes a secure and adaptive intrusion detection model using Federated Learning (FL) and Blockchain, augmented with autoencoder-based feature reduction. The ToN-IoT dataset is pre-processed, and then an unsupervised autoencoder is used to build informative low-dimensional feature representations. The processed data is deployed to various clients to mimic a real federated situation. Every client will train a local Long Short-Term Memory (LSTM) model on its own private data to preserve data privacy.Then a blockchain-based mechanism is utilized to enhance the security and integrity of model aggregation. SHA-256 hashed local model weights are recorded on the blockchain to avoid tampering and provide traceability. Federated averaging is then implemented to refresh the global model along with blockchain-based verification of the aggregation process. Our findings show the performance of the proposed framework, leading to an accuracy of 99.96%, precision of 99.99%, recall of 99.95%, and F1-score of 99.97%. These findings show that combining FL, blockchain, and deep feature extraction offers a viable and secure solution for intrusion detection systems in IoT.

Secure Cloud Storage using Blockchain and AI Threat Detection

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.

Enhancing Cloud Network Security: Utilizing Blockchain for Attack Detection Systems

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.

Cross-Chain and Energy-Efficient Intrusion Detection for IoT Security using Smart Contracts and Machine Learning

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.

Blockchain-Integrated Federated Transfer Learning Framework for Real-Time and Privacy-Preserving Intrusion Detection in Industrial IoT