DOI: 10.3390/a16080366 ISSN: 1999-4893

Machine-Learning Techniques for Predicting Phishing Attacks in Blockchain Networks: A Comparative Study

Kunj Joshi, Chintan Bhatt, Kaushal Shah, Dwireph Parmar, Juan M. Corchado, Alessandro Bruno, Pier Luigi Mazzeo
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Numerical Analysis
  • Theoretical Computer Science

Security in the blockchain has become a topic of concern because of the recent developments in the field. One of the most common cyberattacks is the so-called phishing attack, wherein the attacker tricks the miner into adding a malicious block to the chain under genuine conditions to avoid detection and potentially destroy the entire blockchain. The current attempts at detection include the consensus protocol; however, it fails when a genuine miner tries to add a new block to the blockchain. Zero-trust policies have started making the rounds in the field as they ensure the complete detection of phishing attempts; however, they are still in the process of deployment, which may take a significant amount of time. A more accurate measure of phishing detection involves machine-learning models that use specific features to automate the entire process of classifying an attempt as either a phishing attempt or a safe attempt. This paper highlights several models that may give safe results and help eradicate blockchain phishing attempts.

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