A Blockchain‑Enhanced Deep Learning Approach for Intrusion Detection in Trusted Execution Environments

Traditional Intrusion Detection Systems (IDSs) face significant challenges in keeping pace with the rapidly evolving landscape of cyber threats, primarily due to limitations in continuous learning and the accuracy of data classification and analysis. This often results in delayed detection and leaves networks susceptible to severe attacks. This paper introduces an innovative IDS empowered by blockchain technology to mitigate these shortcomings, leveraging continuous learning and self‑adaptive neural networks. The proposed system adopts a proactive approach by continuously assimilating intrusion logs, utilizing a Long Short‑Term Memory (LSTM) core to discern patterns and enhance its real‑time threat detection capabilities, removing a major bottleneck in traditional IDS models by eliminating the need for manual tagging. To further strengthen the security measures, self‑updating neural networks are embedded in each block of the blockchain, forming a decentralized “brain” that evolves defences against even the most sophisticated adversaries. These networks are securely housed in Trusted Execution Environments (TEEs) to maintain operational integrity, enabling tamper‑proof operation and effective threat detection. Real‑world evaluations conducted on the Binance Smart Chain and Ethereum Classic datasets demonstrate the system’s superior performance. With an impressive accuracy rate of 98.50% and a minimal false positive rate of 1.50%, the model demonstrates a remarkable ability to distinguish legitimate network activity from malicious intrusions.