Real-World Case Study: Evaluating AI-Powered And Traditional Signature Approaches To Email Phishing Threats

15 May
Posted on Author

Authors: Shrushti Kaza, Akhila Harshini Gadamsetty, Abhijeet Raj, Pranav Veer Singh, Dr M Umamaheswari

Abstract: Email-based phishing is among the persistent and costly cybersecurity challenges which exploit human gullibility, social engineering, and organizational frailties for gaining un-approved access to sensitive information. Signature-based cyber-security strategies use preset patterns, blacklists, and heuristic approaches to identify phishing emails. While signature-based detection systems can recognize phishing emails with known char-acteristics successfully, they usually fail to identify sophisticated attacks which evade recognition due to their novelty or disguise. On the other hand, modern technologies based on ML and NLP employ numerous features including email body, natural language used, sender behavior, URLs embedded in an email, and additional metadata. The ability of such approaches to generalize makes them applicable in the detection of previously unseen phishing campaigns. In this study, comprehensive comparison between AI-powered phishing detectors and traditional signature-based methods is conducted using a hand-curated dataset with both legitimate and malicious samples of emails. Criteria for evaluation include detection rates, false positives and negatives, as well as computational resources consumed. The experiments show that AI-based techniques outperform traditional systems in terms of recognizing unknown phishing emails. However, superior performance comes at the expense of greater computational loads and increased requirements for tuning and maintaining AI models. Also, this study provides practical guidance for integrating AI-based phishing detectors into corporate email systems, considering deployment issues, scaling, and computational resources needed. Based on the experiment results presented in the paper, recommendations are made regarding implementation of AI solutions for phishing attacks.

DOI: http://doi.org/10.5281/zenodo.20205928