JOURNAL ARTICLE

Email phishing: text classification using natural language processing

Priyanka VermaAnjali GoyalYogita Gigras

Year: 2020 Journal:   Computer Science and Information Technologies Vol: 1 (1)Pages: 1-12   Publisher: Institute of Advanced Engineering and Science (IAES)

Abstract

Phishing is networked theft in which the main motive of phishers is to steal any person’s private information, its financial details like account number, credit card details, login information, payment mode information by creating and developing a fake page or a fake web site, which look completely authentic and genuine. Nowadays email phishing has become a big threat to all, and is increasing day by day. Moreover detection of phishing emails have been considered an important research issue as phishing emails have been increasing day by day. Various techniques have been introduced and applied to deal with such a big issue. The major objective of this research paper is giving a detailed description on the classification of phishing emails using the natural language processing concepts. NLP (natural language processing) concepts have been applied for the classification of emails, along with that accuracy rate of various classifiers have been calculated. The paper is presented in four sections. An introduction about phishing its types, its history, statistics, life cycle, motivation for phishers and working of email phishing have been discussed in the first section. The second section covers various technologies of phishing- email phishing and also description of evaluation metrics. An overview of the various proposed solutions and work done by researchers in this field in form of literature review has been presented in the third section. The solution approach and the obtained results have been defined in the fourth section giving a detailed description about NLP concepts and working procedure.

Keywords:
Phishing Computer science Credit card Login Payment World Wide Web Artificial intelligence Computer security The Internet

Metrics

31
Cited By
5.94
FWCI (Field Weighted Citation Impact)
10
Refs
0.96
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Spam and Phishing Detection
Physical Sciences →  Computer Science →  Information Systems
Text and Document Classification Technologies
Physical Sciences →  Computer Science →  Artificial Intelligence
Imbalanced Data Classification Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence

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