Long-Range attack detection on permissionless blockchains using Deep Learning

Abstract

Blockchain has been viewed as a breakthrough and an innovative technology due to its privacy, security, immutability, and data integrity characteristics. The consensus layer of the blockchain is the backbone and the most important layer of the blockchain architecture because it acts as the performance and security manager of the blockchain. The detection of Long-Range Attacks (LRA) on the Proof-of-Stake (PoS) blockchain is a complex task. Earlier studies have shown various challenges in detecting long-range attacks and monitoring the activities of validator nodes on the blockchain network. Thus, this paper proposes a novel dataset for node classification on a proof-of-stake permissionless blockchain and proposes a Deep Learning method that can be used to classify nodes into malicious or non-malicious nodes to mitigate long-range attacks with high accuracy. The performance metrics for the model are compared and measured which suggest the developed performance of the proposed model. The proposed solution can serve as a guide on how future researchers and blockchain developers can simulate and curate proof-of-stake datasets and goes further to demonstrate that artificial intelligence models can be used as a mitigating checkpoint for long-range attacks. The dataset in the paper is publicly available and can be used by other researchers to detect other activities and behaviors on a permissionless blockchain. These techniques can further enhance security, performance and create fairness on the proof-of-stake consensus.