Name
Abstract | ||
|---|---|---|
Phishing email is one of the major problems of today's Internet, resulting in financial losses for organizations and annoying individual users. Numerous approaches have been developed to filter phishing emails, yet the problem still lacks a complete solution. In this paper, we present a survey of the state of the art research on such attacks. This is the first comprehensive survey to discuss methods of protection against phishing email attacks in detail. We present an overview of the various techniques presently used to detect phishing email, at the different stages of attack, mostly focusing on machine-learning techniques. A comparative study and evaluation of these filtering methods is carried out. This provides an understanding of the problem, its current solution space, and the future research directions anticipated. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SURV.2013.030713.00020 | IEEE Communications Surveys and Tutorials |
Keywords | Field | DocType |
machine learning,authentication,financial losses,classifiers,learning (artificial intelligence),computer crime,unsolicited e-mail,machine-learning techniques,internet,phishing email attacks,phishing email,filtering,phishing email filtering techniques,network level protection,classification,learning artificial intelligence,phishing | Email filtering,World Wide Web,Phishing,Computer security,Computer science,Spoofed URL,Email authentication,Email spoofing,The Internet | Journal |
Volume | Issue | ISSN |
15 | 4 | 1553-877X |
Citations | PageRank | References |
33 | 1.04 | 36 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ammar Almomani | 1 | 116 | 8.68 |
| B. B. Gupta | 2 | 518 | 46.49 |
| Samer Atawneh | 3 | 63 | 1.80 |
| A. Meulenberg | 4 | 33 | 1.38 |
| Eman Almomani | 5 | 33 | 1.04 |