Deep Learning-Based Detection of Malicious Nodes in PoS Permissionless Blockchains

Abstract

Identifying malevolent nodes on blockchain networks is essential for preserving the integrity and safeguarding the security of decentralized systems. By utilizing advanced algorithms and analysis approaches, such as machine learning and deep learning models, it is possible to identify abnormal behaviours that suggest hostile intent. Through the analysis of transaction patterns, node behaviour, and network interactions, these detection techniques may accurately distinguish between legal nodes and those involved in malicious activities like as double spending, Sybil attacks, or denial-of-service operations. By taking a proactive approach, potential threats can be effectively mitigated, which in turn promotes confidence and reliability in the blockchain ecosystem. This ultimately leads to widespread acceptance and scaling of the technology. This paper Provides a detailed explanation of the methodology used to enhance blockchain security by identifying and addressing rogue nodes. The analysis commenced with an exploratory examination of the data, which allowed for a comprehensive grasp of the numerical and categorical characteristics of the dataset. Afterwards, the Multi-Layer Perceptron (MLP) method was utilized, showcasing exceptional performance with an accuracy of around 98% for both the training and validation datasets. In addition, the model demonstrated minimum loss values, at 0.06%, which suggests strong learning and generalization ability. The results emphasize the effectiveness of deep learning methods in strengthening blockchain networks against hostile actions, therefore guaranteeing their integrity and dependability.