PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
January 1, 2021IEEE Transactions on Dependable and Secure Computing46 citations

Phishing Email Detection using Persuasion Cues

View Full Paper
RVRohit ValechaPMPranali MandaokarHRH. Raghav Rao

Key Points

Key points are not available for this paper at this time.

Abstract

Phishing is an attempt to acquire sensitive information from an unsuspecting victim by malicious means. Recent studies have shown that phishers often use persuasion techniques to get positive responses from the recipients. Still missing from this literature are studies assessing effectiveness of persuasion cues in phishing email detection. Specifically focusing on gain and loss persuasion cues, we address the following research questions: In detecting phishing emails, (1) how effective are the gain persuasion cues, (2) how effective are the loss persuasion cues, and (3) how effective is an integrated model of gain and loss persuasion In order to address the research questions, we create three machine learning models, with relevant gain persuasion cues, loss persuasion cues, and combined gain and loss persuasion cues respectively, and compare the estimates with a baseline model that does not account for the persuasion cues. The results show that the three phishing detection models with relevant persuasion cues significantly outperform the baseline model by approximately 5% to 20% percent in terms of F-score, thus representing reliable methods for phishing email detection.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Valecha et al. (2021) studied this question.

synapsesocial.com/papers/6a12d5ee8793652519a67f2fhttps://doi.org/10.1109/tdsc.2021.3118931
Ask AI
Helpful
Bookmark
Share
View Full Paper