A Model for Detecting Sounds-alike Phishing Email Contents for Persons with Visual Impairments

• Published in: 2020 Sixth International Conference on e-Learning (econf) pp. 17 - 21
• Main Author: Sonowal, Gunikhan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06.12.2020
• Subjects: Cybersecurity; Edit Distance; Electronic mail; Machine Learning; Matched filters; Pattern matching; Phishing; Phishing Email; Uniform resource locators; Visualization
• DOI: 10.1109/econf51404.2020.9385451

In the Internet environment, most people, organizations communicate with each other through emails. Although Email is a significant mechanism for communication, a phishing email is one of the exponential growth in cybercrime. In phishing, attackers randomly send fraud emails to victims by practicing a legitimate site's identification which encourages the user to believe in it and disclose their credentials. As increasing of phishing emails, some kinds of phishers exercise sound-alike contents within the email to target persons with visual impairments. A fundamental shortcoming of the existing research is that it does not address the difficulty of sound-alike keywords. Therefore, this paper proposes a model titled SPEDAS(Sounds-alike contents Phishing Email Detection Assist to Screen reader users). This Model detects phishing emails by verifying sound-alike contents. The result of the experiment shows that the proposed model provided 83% accuracy.