Classification of Phishing Email Using Word Embedding and Machine Learning Techniques

Authors

  • Somesha M. Information Security Research Lab, Department of Computer Science and Engineering, National Institute of Technology karnataka, Surathkal, Karnataka, India, 575025
  • Alwyn R. Pais Information Security Research Lab, Department of Computer Science and Engineering, National Institute of Technology karnataka, Surathkal, Karnataka, India, 575025 https://orcid.org/0000-0003-4571-4608

DOI:

https://doi.org/10.13052/jcsm2245-1439.1131

Keywords:

Email phishing detection, Word embedding, Machine Learning, Word2ec, FastText, TF-IDF, Count Vectorization

Abstract

Email phishing is a cyber-attack, bringing substantial financial damage to corporate and commercial organizations. A phishing email is a special type of spamming, used to trick the user to disclose personal information to access his digital assets. Phishing attack is generally triggered by emailing links to spoofed websites that collect sensitive information. The APWG survey suggests that the existing countermeasures remain ineffective and insufficient for detecting phishing attacks. Hence there is a need for an efficient mechanism to detect phishing emails to provide better security against such attacks to the common user. The existing open-source data sets are limited in diversity, hence they do not capture the real picture of the attack. Hence there is a need for real-time input data set to design accurate email anti-phishing solutions. In the current work, it has been created a real-time in-house corpus of phishing and legitimate emails and proposed efficient techniques to detect phishing emails using a word embedding and machine learning algorithms. The proposed system uses only four email header-based heuristics for the classification of emails. The proposed word embedding cum machine learning framework comprises six word embedding techniques with five machine learning classifiers to evaluate the best performing combination. Among all six combinations, Random Forest consistently performed the best with FastText (CBOW) by achieving an accuracy of 99.50% with a false positive rate of 0.053%, TF-IDF achieved an accuracy of 99.39% with a false positive rate of 0.4% and Count Vectorizer achieved an accuracy of 99.18% with a false positive rate of 0.98% respectively for three datasets used.

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Author Biographies

Somesha M., Information Security Research Lab, Department of Computer Science and Engineering, National Institute of Technology karnataka, Surathkal, Karnataka, India, 575025

Somesha M. is an Assistant Professor & HOD, Department of Computer Science and Engineering, Government Engineering College, Karwar, Karnataka, India. He completed his B.E.(CSE) from Bangalore University, India and M.Tech.(CNE) from NIE Mysore, India. He is currently pursuing Ph.D. from the Department of Computer Science & Engineering, National Institute of Technology Karnataka (NITK), Surathkal. His areas of interest include Information Security, Computer Networks, and Cyber security.

Alwyn R. Pais, Information Security Research Lab, Department of Computer Science and Engineering, National Institute of Technology karnataka, Surathkal, Karnataka, India, 575025

Alwyn R. Pais is an Associate Professor and Research Guide, Department of Computer Science and Engineering, National Institute of Technology Karnataka (NITK). He completed his B.Tech.(CSE) from Mangalore University, India, M.Tech. (CSE) from IIT Bombay, India, and Ph.D. (CSE) in NITK, Surthkal. His area of interest includes Information Security, Image Processing and Computer Vision.

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Published

2022-05-07

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