Phishing Email Detection Using Natural Language Processing Techniques: A Literature Survey

被引:43
作者
Salloum, Said [1 ]
Gaber, Tarek [1 ,2 ]
Vadera, Sunil [1 ]
Shaalan, Khaled [3 ]
机构
[1] Univ Salford, Sch Sci Engn & Environm, Salford, Lancs, England
[2] Suez Canal Univ, Fac Comp & Informat, Ismailia 41522, Egypt
[3] British Univ Dubai, Fac Engn & IT, Dubai, U Arab Emirates
来源
AI IN COMPUTATIONAL LINGUISTICS | 2021年 / 189卷
关键词
Phishing email; Natural Language Processing; Machine Learning; CLASSIFICATION; PREDICTION; VECTORS; MODEL;
D O I
10.1016/j.procs.2021.05.077
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Phishing is the most prevalent method of cybercrime that convinces people to provide sensitive information; for instance, account IDs, passwords, and bank details. Emails, instant messages, and phone calls are widely used to launch such cyber-attacks. Despite constant updating of the methods of avoiding such cyber-attacks, the ultimate outcome is currently inadequate. On the other hand, phishing emails have increased exponentially in recent years, which suggests a need for more effective and advanced methods to counter them. Numerous methods have been established to filter phishing emails, but the problem still needs a complete solution. To the best of our knowledge, this is the first survey that focuses on using Natural Language Processing (NLP) and Machine Learning (ML) techniques to detect phishing emails. This study provides an analysis of the numerous state-of-the-art NLP strategies currently in use to identify phishing emails at various stages of the attack, with an emphasis on ML strategies. These approaches are subjected to a comparative assessment and analysis. This gives a sense of the problem, its immediate solution space, and the expected future research directions. (C) 2021 The Authors. Published by Elsevier B.V.
引用
收藏
页码:19 / 28
页数:10
相关论文
共 53 条
[1]   Phishing Attacks Survey: Types, Vectors, and Technical Approaches [J].
Alabdan, Rana .
FUTURE INTERNET, 2020, 12 (10) :1-39
[2]   An Effective Phishing Detection Model Based on Character Level Convolutional Neural Network from URL [J].
Aljofey, Ali ;
Jiang, Qingshan ;
Qu, Qiang ;
Huang, Mingqing ;
Niyigena, Jean-Pierre .
ELECTRONICS, 2020, 9 (09) :1-24
[3]  
[Anonymous], 2016, APWG PHISH TREND REP
[4]  
[Anonymous], 2011, P IJCST
[5]  
[Anonymous], 2014, ABS14090473 CORR
[6]  
Anti-Phishing Working Group, 2020, PHIS ACT TRENDS REP
[7]  
Bergholz A., 2008, P 2008 C EMAIL ANT
[8]   New filtering approaches for phishing email [J].
Bergholz, Andre ;
De Beer, Jan ;
Glahn, Sebastian ;
Moens, Marie-Francine ;
Paass, Gerhard ;
Strobel, Siehyun .
JOURNAL OF COMPUTER SECURITY, 2010, 18 (01) :7-35
[9]  
Chollet F, 2018, DEEP LEARNING PYTHON, V361
[10]  
Coyotes C., 2018, P 1 ANTIPHISHING SHA