Improving Phishing Detection in Ethereum transaction network using Quantum Machine Learning

Abstract

Ethereum is one of the most valuable blockchain networks in terms of the total monetary value locked in it, and arguably been the most active network where new blockchain innovations in research and applications are demonstrated. But, this also leads to such transactional networks being susceptible to a wide variety of threats and attacks in an attempt to gain unreasonable advantage or to undermine the value of the users. Even with the state-of-art classical ML algorithms, detecting such attacks is hard. This motivated us explore quantum machine learning algorithms that can probably be better for such hard problems. This paper proposes to improve phishing detection and performance of QML algorithms such as Quantum Support Vector Machine (QSVM) and Variational Quantum Classifier (VQC) by using a cascaded form of Quantum Random Access Coding (QRAC) encoding scheme. We present detailed ablation studies with other encoding strategies, and comparing with classical ML algorithms and quantum algorithms on simulators and real quantum hardware. For QSVM, we have tried two different implementations of QSVM - one using Kernel estimation using quantum circuits on universal quantum computers, and the other using annealers and for VQC, we benchmarked with different ansatzes with different entangling capacities. For both algorithms we tried different feature maps but consistently observe that QRAC-VQC gives 13% improvement and QRAC-QSVM gives 3% improvement over baselines. Overall we observe that QSVM gives least false negatives thus better at detecting phishing nodes even over classical state-of-art graph convolutional networks. Another important and interesting observation is that the performance of these QML algorithms does not drastically drop with currently available noisy hardware and is almost equal to simulator for a quantum device with high quantum volume or performance.