Pith. sign in

REVIEW 1 cited by

Do Users Focus on the Correct Cues to Differentiate Between Phishing and Genuine Emails?

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1605.04717 v1 pith:XZOBMP36 submitted 2016-05-16 cs.CY cs.CR

classification cs.CYcs.CR
keywords cuesemailsgenuinephishingdifferentiateemailidentifyparticipants
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

This paper examines the cues that typically differentiate phishing emails from genuine emails. The research is conducted in two stages. In the first stage, we identify the cues that actually differentiate between phishing and genuine emails. These are the consistency and personalisation of the message, the perceived legitimacy of links and sender, and the presence of spelling or grammatical irregularities. In the second stage, we identify the cues that participants use to differentiate between phishing and genuine emails. This revealed that participants often use cues that are not good indicators of whether an email is phishing or genuine. This includes the presence of legal disclaimers, the quality of visual presentation, and the positive consequences emphasised in the email. This study has implications for education and training and provides a basis for the design and development of targeted and more relevant training and risk communication strategies.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Beyond Detection: Evaluating Defensive LLMs Against AI-Generated Social Engineering in Live Turn-by-Turn Interaction

    cs.AI 2026-08 conditional novelty 7.0 of 10

    Defensive LLMs frequently intervene without identifying the right compromised trust component, and sometimes correctly diagnose a failure while still recommending no protective action.

Pith tools