AES-Enhanced Blockchain Intrusion Detection System for Secure Networks

Abstract

Blockchain is a distributed ledger technology that can securely, transparently, and tamper-proofly record transactions across a network. IDS on blockchain monitors nodes and activities in transactions to detect malicious or unusual patterns, thereby improving network integrity. However, blockchain also has disadvantages such as high computational overhead, vulnerability to specific attacks, and limited scalability. As a method to enhance IDS performance, the Z-score is used to normalize feature values, stabilizing the model and facilitating convergence. A combination of Support Vector Machines (SVM) and Decentralized Identification (DID) architecture can effectively and reliably distinguish between normal node behavior and malicious node behavior. Key verification uses consensus-driven transaction authentication, and a Multi-Layer Perceptron Neural Network (MLPNN) can detect complex attack patterns. All sensitive information is protected using the Advanced Encryption Standard (AES) to provide robust encryption for both stored and transmitted data. By combining these methods, blockchain applications can offer a comprehensive, secure, and scalable network intrusion detection system, addressing the limitations of detection accuracy, computational efficiency, and privacy in existing data and traditional blockchain implementations, to achieve an accuracy of 91%.