Authors :
Praveen N; Kartik S N; Santosh V; Kishore N; Dr. Prakasha S
Volume/Issue :
Volume 9 - 2024, Issue 12 - December
Google Scholar :
https://tinyurl.com/3swtxj98
Scribd :
https://tinyurl.com/bdfkr64u
DOI :
https://doi.org/10.5281/zenodo.14511771
Abstract :
Phishing involves fraudulent activities where
attackers impersonate trustworthy websites to
unlawfully obtain private information, including
usernames, passwords, and financial details. Traditional
detection methods, including blacklists and heuristic-
based approaches, struggles identifying new, evolving
phishing sites. In recent times, AI using machine
learning (ML) has emerged as a powerful tool for
phishing detection, offering predictive capabilities that
adapt to changing attack patterns. This survey examines
state- of-the-art ML techniques for phishing website
detection, covering feature extraction, model types, and
challenges in data handling. Through analyzing recent
methodologies, this paper highlights the strengths and
limitations of various ML models and proposes
directions for further improving phishing detection
systems.
Keywords :
Phishing Detection, Machine Learning, Cybersecurity, Feature Extraction, Classification Models, URL Analysis.
References :
- Tang, L., Mahmoud, Q. H. "A Survey of Machine Learning- Based Solutions for Phishing Website Detection." Machine Learning & Knowledge Extraction, 2021. This paper reviews the life cycle of phishing attacks.
- Vijayalakshmi, T., et al. "Taxonomy of Automated Phishing Detection Solutions." Journal of Cybersecurity, 2020. This paper categorizes phishing detection methods into URL-based, content- based, and hybrid approaches, comparing the strengths of each.
- Jain, A., Gupta, P. "Reinforcement Learning for Phishing Detection." International Journal of Computer Science Research, 2022. The authors explore reinforcement learning for phishing detection, noting its adaptability in evolving phishing tactics.
- Kalaharsha, A., Mehtre, B. M. "Unsupervised Learning Techniques for Phishing Detection." Journal of Information Security and Applications, 2021. This research examines unsupervised learning approaches that cluster phishing data without labels, offering insights into alternative detection methods.
- Bingyang, L. "Natural Language Processing in Phishing Detection." Journal of Emerging Technologies in Computing Systems, 2024. This paper focuses on NLP techniques for extracting phishing features from text and URLs, addressing challenges in model training.
- Patel, R., et al. "Ensemble Models for Phishing Detection and Security Awareness." Cybersecurity Journal, 2024. The study reviews ensemble methods, combining machine learning models with security checks to prevent vulnerabilities in generated code.
- El Asri, L., et al. "Multi-Turn Dialogue for Clarifying User Intent in Phishing Detection Systems." Computational Intelligence Journal, 2024. This paper proposes using dialogue models for interpreting ambiguous user prompts in phishing detection, enhancing model accuracy in complex scenarios.
Phishing involves fraudulent activities where
attackers impersonate trustworthy websites to
unlawfully obtain private information, including
usernames, passwords, and financial details. Traditional
detection methods, including blacklists and heuristic-
based approaches, struggles identifying new, evolving
phishing sites. In recent times, AI using machine
learning (ML) has emerged as a powerful tool for
phishing detection, offering predictive capabilities that
adapt to changing attack patterns. This survey examines
state- of-the-art ML techniques for phishing website
detection, covering feature extraction, model types, and
challenges in data handling. Through analyzing recent
methodologies, this paper highlights the strengths and
limitations of various ML models and proposes
directions for further improving phishing detection
systems.
Keywords :
Phishing Detection, Machine Learning, Cybersecurity, Feature Extraction, Classification Models, URL Analysis.