Smart Computing- Book ISBN- 978-81-953658-1-4

Authors:- Amita Contractor, Rutvik Mehta, Sumitra Menaria

Abstract :-Phishing attack detection is one of the most challenging challenges currently facing online social network users. This study presents a different phishing detection technique to identify gaps and offer solutions to current phishing detection problems. As shown, no techniques have been proposed to address phishing, including Machine Learning (ML) based, Nature Inspired (NI) based, heuristic, blacklist based and whitelist based techniques. However, the ML-based techniques give more accuracy in terms of classification.ML algorithms cannot deal with big datasets; hence they can be combined with NI algorithms to build fast and improved models for phishing detection. Although some surveys on phishing detection techniques exist, very few focused on ML-based and Ni-based techniques. Therefore, this study presents ML-based and NI-based phishing detection techniques. The survey reveals the various shortcomings of phishing detection techniques, including limited dataset, use of third-party services (age of the domain, search engine query, etc.), use of small feature set, use of classification rules, use of blacklist and whitelist, etc. There is an obvious need for efficient and reliable solutions for phishing detection.

Keywords: - Phishing detection; Phishing website; Phishing email; Machine learning; Nature-inspired techniques; Social engineering