Classical ensemble of Quantum-classical ML algorithms for Phishing detection in Ethereum transaction networks
Anupama Ray, Sai Sakunthala Guddanti, Vishnu Ajith, Dhinakaran, Vinayagamurthy

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…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Quantum Computing Algorithms and Architecture · EEG and Brain-Computer Interfaces
MethodsTest
