Classical ensemble of Quantum-classical ML algorithms for Phishing detection in Ethereum transaction networks

TL;DR

This paper introduces a hybrid quantum-classical ensemble system for phishing detection in Ethereum networks, demonstrating improved accuracy and the largest quantum machine learning experiment on real hardware with 12K data points.

Contribution

It presents a novel ensemble pipeline combining classical and quantum algorithms, benchmarking quantum models on large datasets, and highlighting QSVM's superior false positive reduction.

Findings

Quantum ensemble models improved macro F-score and phishing detection accuracy.

QSVM consistently achieved lower false positives and higher precision.

Largest quantum machine learning experiment on real hardware with 12K data points.

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 Ethereum network 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 still hard. This motivated us to build a hybrid system of quantum-classical algorithms that improves phishing detection in financial transaction networks. This paper presents a classical ensemble pipeline of classical and quantum algorithms and a detailed study benchmarking existing Quantum Machine Learning algorithms such as Quantum Support Vector Machine and Variational Quantum Classifier. With the current generation of quantum hardware available, smaller datasets are more suited to the QML models and most research restricts to hundreds of samples. However, we experimented on different data sizes and report results with a test data of 12K transaction nodes, which is to the best of the authors knowledge the largest QML experiment run so far on any real quantum hardware. The classical ensembles of quantum-classical models improved the macro F-score and phishing F-score. One key observation is QSVM constantly gives lower false positives, thereby higher precision compared with any other classical or quantum network, which is always preferred for any anomaly detection problem. This is true for QSVMs when used individually or via bagging of same models or in combination with other classical/quantum models making it the most advantageous quantum algorithm so far. The proposed ensemble framework is generic and can be applied for any classification task