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A Deep-Learning-Driven Light-Weight Phishing Detection Sensor.

Bo Wei1, Rebeen Ali Hamad2, Longzhi Yang3

  • 1Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK. bo.wei@northumbria.ac.uk.

Sensors (Basel, Switzerland)
|October 3, 2019
PubMed
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This summary is machine-generated.

This study introduces a new, efficient deep learning method for detecting phishing attempts by analyzing malicious URLs. The developed system offers improved accuracy and real-time, energy-saving performance for enhanced online security.

Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Phishing is a prevalent social engineering attack using deceptive Uniform Resource Locators (URLs) and webpages.
  • Traditional phishing detection relies heavily on manual user reports, which is often inefficient.
  • Machine learning and deep learning techniques are increasingly explored to enhance phishing detection accuracy.

Purpose of the Study:

  • To design an accurate and low-cost phishing detection sensor.
  • To develop a light-weight deep learning algorithm for real-time malicious URL detection.
  • To enable an energy-saving phishing detection sensor for embedded systems.

Main Methods:

  • Exploration of deep learning techniques for phishing detection.
Keywords:
cyber securitydeep learningphishing detection

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  • Development of a light-weight deep learning algorithm.
  • Experimental testing and comparison of the proposed method's efficacy.

    • Main Results:

    • The proposed deep learning method significantly improved the true detection rate for phishing URLs.
    • The algorithm demonstrated real-time performance capabilities.
    • The system was verified to run effectively on an energy-saving embedded single board computer.

    Conclusions:

    • The developed light-weight deep learning algorithm provides an accurate and efficient solution for phishing detection.
    • The proposed method enables real-time and energy-saving phishing detection sensors.
    • This approach enhances online security against sophisticated social engineering attacks.