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A comprehensive survey of AI-enabled phishing attacks detection techniques.
Abdul Basit1, Maham Zafar1, Xuan Liu2
1Department of Computer Science, Air University, E-9, Islamabad, Pakistan.
Telecommunication Systems
|October 28, 2020
Summary
This paper reviews Artificial Intelligence (AI) techniques like Machine Learning and Deep Learning for detecting phishing attacks. It compares their effectiveness and discusses current challenges and future research in cybersecurity.
Area of Science:
- Cybersecurity
- Artificial Intelligence
Background:
- Phishing attacks are a major threat, involving data theft via fake websites and emails.
- Phishing websites are key to social engineering and online fraud.
Purpose of the Study:
- To review Artificial Intelligence (AI) techniques for phishing attack detection.
- To compare different AI methodologies and analyze their strengths and weaknesses.
Main Methods:
- Literature review of AI techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based.
- Comparative analysis of studies employing these AI techniques for phishing detection.
Main Results:
- AI techniques show promise in detecting phishing attacks.
- Different AI methods have varying degrees of effectiveness and limitations.
Conclusions:
- AI offers a robust approach to combating phishing threats.
- Further research is needed to address current challenges and advance AI-driven phishing detection.
