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Proactive detection of anomalous behavior in Ethereum accounts using XAI-enabled ensemble stacking with Bayesian optimization.

PeerJ. Computer scienceยท2025
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Updated: Sep 18, 2025

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Enhancing phishing detection with dynamic optimization and character-level deep learning in cloud environments.

Vishnukumar Ravula1, Mangayarkarasi Ramaiah1

  • 1School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology University, Vellore, Tamil Nadu, India.

Peerj. Computer Science
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Dynamic Arithmetic Optimization Algorithm with Deep Learning-Driven Phishing URL Classification (DAOA-DLPC) model to detect phishing URLs in cloud-enabled Internet of Vehicles (IoV) environments. The DAOA-DLPC model achieves high accuracy in identifying safe and phishing URLs, enhancing security.

Keywords:
Arithmetic optimization algorithmCloud computingCybersecurityDeep learningPhishing attacks

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Area of Science:

  • Cybersecurity
  • Cloud Computing
  • Internet of Vehicles (IoV)

Background:

  • Cloud computing and IoV environments face increasing phishing URL threats.
  • Traditional machine learning methods struggle with dynamic phishing landscapes.
  • Service reliability in IoV is jeopardized by phishing attacks.

Purpose of the Study:

  • To propose a novel Dynamic Arithmetic Optimization Algorithm with Deep Learning-Driven Phishing URL Classification (DAOA-DLPC) model.
  • To enhance phishing URL detection in cloud-enabled IoV infrastructure.
  • To improve the accuracy and efficiency of identifying malicious URLs.

Main Methods:

  • Utilized character-level embeddings for effective URL pattern capture.
  • Integrated embeddings with a Multi-Head Attention and Bidirectional Gated Recurrent Units (MHA-BiGRU) deep learning model.
  • Employed Dynamic Arithmetic Optimization Algorithm (DAOA) for hyperparameter tuning.

Main Results:

  • The DAOA-DLPC model achieved 98.85% accuracy, 98.49% recall, and 98.38% F1-score.
  • Demonstrated superior performance compared to conventional models in dynamic environments.
  • Showcased computational efficiency via attention mechanisms and dynamic optimization.

Conclusions:

  • The DAOA-DLPC model provides a feasible and effective solution for phishing URL detection in IoV.
  • The model can learn new phishing attack forms in real-time and reduce false positives.
  • The proposed method significantly improves the distinction between safe and unsafe URLs.