A Decentralized and Self-Adaptive Intrusion Detection Approach Using Continuous Learning and Blockchain Technology

Abstract

The landscape of cyber threats is constantly in flux, which can cause traditional intrusion detection systems (IDS) to fall behind the rapid evolution of attacks. This can result in delayed detection and devastating consequences. The proposed system leverages continuous learning and self-adaptive neural networks to transcend the limitations of traditional IDS. It takes a proactive approach by continuously analyzing intrusion logs, using a Long Short-Term Memory (LSTM) core to identify emerging patterns and refine its understanding of threats in real time. This eliminates the static limitations of traditional models and encourages continuous improvement. The system's neural network is distributed across each block of the blockchain, creating a decentralized 'brain' that develops defenses against advanced adversaries. Secure enclaves are located within Trusted Execution Environments (TEEs), ensuring tamper-proof operation and reliable threat detection. The system's superior performance is demonstrated through rigorous evaluations of established datasets, such as NSL-KDD. The model demonstrates a high level of accuracy of 0.9994 with a minimal false positive rate of 0.06, indicating its ability to differentiate between legitimate network activity and malicious intrusions. Embracing continuous learning and a decentralized architecture creates a dynamic and resilient system that proactively adapts to the ever-changing threat landscape. This approach has several advantages over traditional solutions, including enhanced precision, increased security, and real-time adaptability.