English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Ethereum is one of the most valuable blockchain networks in terms of thetotal monetary value locked in it, and arguably been the most active networkwhere new blockchain innovations in research and applications are demonstrated.But, this also leads to Ethereum network being susceptible to a wide variety ofthreats and attacks in an attempt to gain unreasonable advantage or toundermine the value of the users. Even with the state-of-art classical MLalgorithms, detecting such attacks is still hard. This motivated us to build ahybrid system of quantum-classical algorithms that improves phishing detectionin financial transaction networks. This paper presents a classical ensemblepipeline of classical and quantum algorithms and a detailed study benchmarkingexisting Quantum Machine Learning algorithms such as Quantum Support VectorMachine and Variational Quantum Classifier. With the current generation ofquantum hardware available, smaller datasets are more suited to the QML modelsand most research restricts to hundreds of samples. However, we experimented ondifferent data sizes and report results with a test data of 12K transactionnodes, which is to the best of the authors knowledge the largest QML experimentrun so far on any real quantum hardware. The classical ensembles ofquantum-classical models improved the macro F-score and phishing F-score. Onekey observation is QSVM constantly gives lower false positives, thereby higherprecision compared with any other classical or quantum network, which is alwayspreferred for any anomaly detection problem. This is true for QSVMs when usedindividually or via bagging of same models or in combination with otherclassical/quantum models making it the most advantageous quantum algorithm sofar. The proposed ensemble framework is generic and can be applied for anyclassification task

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