A Comparative Study of Traditional and AI-Based Phishing Detection Techniques

By: Gonipalli Bharath Vel Tech University, Chennai, India International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan, Gmail: gonipallibharath@gmail.com

Abstract:

Phishing is still one of the principal cybersecurity threats, leveraging human behavior and system vulnerabilities. Traditional methods for detecting phishing rely on rule-based filtering, blacklists, and signature-based techniques, which cannot detect innovative and new methods of phishing. Phishing detection with AI relies on machine learning and deep learning models to inspect patterns, recognize anomalies, and predict phishing activity more effectively. This short article that follows outlines the variations and resemblance among conventional as well as artificial intelligence-based approaches along with their efficacy, versatility, adaptability, and appropriateness in real-world situations. This detailed model highlights the increasing importance of smarter security procedures and shows the transition to traditional to artificial intelligence-based approaches.

Introduction:

Phishing tricks customers into revealing personal information such as passwords, credit card numbers, and personal identification. Phishing has evolved overtime from simple scams in the form of emails to sophisticated social engineering methods[1]. Traditional approaches rely drastically on pre-calculated policies as well as proven attack signatures and therefore are worthless against new-style phishing attacks[2]. AI-based solutions, however, scan gigantic datasets, detect anomalies, and learn new attack patterns dynamically. This study compares these two approaches to determine the strength and weakness of each.

Traditional Phishing Detection Techniques:

• Blacklist-Based Detection: Maintains a list of known phishing websites but fails against new threats[3].
• Heuristic-Based Detection: Uses predefined rules to identify suspicious URLs, though it struggles with advanced obfuscation techniques[4].
• Signature-Based Detection: Recognizes known phishing patterns but lacks adaptability[5].
• Human-Involved Filtering: Relies on user awareness and manual reporting, often slow and inconsistent.

AI-Based Phishing Detection Techniques:

• Machine Learning Models: For classifying fraudulent messages, examine web addresses, content of emails, and spammer activity.
• Deep Learning Methods: Artificial neural networks recognize abnormalities as well as hidden trends throughout information.
• Natural Language Processing: The use of natural language processing or NLP, uses linguistic trends, tone, and context to detect phony texts.
• Real-Time Adaptive Learning: Improves models frequently to thwart new malware tactics.

Comparative Analysis:

Feature	Traditional Methods	AI-Based Methods
Adaptability	Low	High
Accuracy	Moderate	High
Real-Time Detection	Limited	Advanced
Handling New Attacks	Weak	Strong

Traditional vs. AI-Based Phishing Detection:

Conclusion:

Through simple fraudulent emails to AI-powered fake content tricks, malware attacks have developed over time. Proactive security measures, AI-driven detection systems, and user education continue to be essential in the fight against contemporary spoofing risks since fraudsters utilize cutting-edge technologies. AI-powered phishing detection methods surpass conventional methods in terms of adaptability, real-time detection, and addressing new phishing strategies. As cyber threats change, AI-powered methods offer a strong defense, guaranteeing improved cybersecurity resilience.

References:

1. Z. Alkhalil, C. Hewage, L. Nawaf, and I. Khan, “Phishing Attacks: A Recent Comprehensive Study and a New Anatomy,” Front. Comput. Sci., vol. 3, Mar. 2021, doi: 10.3389/fcomp.2021.563060.
2. K. L. Chiew, K. S. C. Yong, and C. L. Tan, “A survey of phishing attacks: Their types, vectors and technical approaches,” Expert Syst. Appl., vol. 106, pp. 1–20, Sep. 2018, doi: 10.1016/j.eswa.2018.03.050.
3. W. Li, S. Ul Arfeen Laghari, S. Manickam, Y.-W. Chong, and B. Li, “Machine Learning-Enabled Attacks on Anti-Phishing Blacklists,” IEEE Access, vol. 12, pp. 191586–191602, 2024, doi: 10.1109/ACCESS.2024.3516754.
4. A. S. Rafsanjani, N. Binti Kamaruddin, M. Behjati, S. Aslam, A. Sarfaraz, and A. Amphawan, “Enhancing Malicious URL Detection: A Novel Framework Leveraging Priority Coefficient and Feature Evaluation,” IEEE Access, vol. 12, pp. 85001–85026, 2024, doi: 10.1109/ACCESS.2024.3412331.
5. M. E. Edge and P. R. Falcone Sampaio, “A survey of signature based methods for financial fraud detection,” Comput. Secur., vol. 28, no. 6, pp. 381–394, Sep. 2009, doi: 10.1016/j.cose.2009.02.001.
6. Sedik, A., Hammad, M., Abd El-Samie, F. E., Gupta, B. B., & Abd El-Latif, A. A. (2022). Efficient deep learning approach for augmented detection of Coronavirus disease. Neural Computing and Applications, 1-18.
7. Deveci, M., Pamucar, D., Gokasar, I., Köppen, M., & Gupta, B. B. (2022). Personal mobility in metaverse with autonomous vehicles using Q-rung orthopair fuzzy sets based OPA-RAFSI model. IEEE Transactions on Intelligent Transportation Systems, 24(12), 15642-15651.
8. Karthik V. (2025) Deepfake Phishing : When AI Voices and videos Trick You, Insights2techinfo pp.1

Cite As

Bharath G. (2025) A Comparative Study of Traditional and AI-Based Phishing Detection Techniques, Insights2Techinfo, pp.1

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