An effective MLP model for detecting malicious nodes in PoS permissionless blockchains

Abstract

With the proliferation of blockchain technology, ensuring the security and integrity of permissionless Proof-of-Stake (PoS) blockchain networks has become imperative. This paper addresses the persistent need for an effective system to detect and mitigate malicious nodes in such environments. Leveraging Deep Learning (DL) techniques, specifically Multi-Layer Perceptron (MLP), a novel model is proposed for real-time identification and detection of malicious nodes in PoS blockchain networks. The model integrates components for data collection, feature extraction, and model training using MLP. The proposed model is trained on labelled data representing both benign and malicious node activities, utilising transaction volumes, frequencies, timestamps, and node reputation scores to identify anomalous behaviour indicative of malicious activity. The experimental results validate the efficacy of the proposed model in distinguishing between normal and malicious nodes within blockchain networks. The model demonstrates exceptional performance in classification tasks with an accuracy of 99%, precision, recall, and F1-score values hovering around 0.99 for both classes. The experimental results verify the proposed model as a dependable tool for enhancing the security and integrity of PoS blockchain networks, offering superior performance in real-time detection and mitigation of malicious activities.