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1. How Experts Detect Phishing Scam Emails

   https://doi.org/10.1145/3415231  

   Wash, Rick (October 2020, Proceedings of the ACM on Human-Computer Interaction)

   Phishing scam emails are emails that pretend to be something they are not in order to get the recipient of the email to undertake some action they normally would not. While technical protections against phishing reduce the number of phishing emails received, they are not perfect and phishing remains one of the largest sources of security risk in technology and communication systems. To better understand the cognitive process that end users can use to identify phishing messages, I interviewed 21 IT experts about instances where they successfully identified emails as phishing in their own inboxes. IT experts naturally follow a three-stage process for identifying phishing emails. In the first stage, the email recipient tries to make sense of the email, and understand how it relates to other things in their life. As they do this, they notice discrepancies: little things that are off'' about the email. As the recipient notices more discrepancies, they feel a need for an alternative explanation for the email. At some point, some feature of the email --- usually, the presence of a link requesting an action --- triggers them to recognize that phishing is a possible alternative explanation. At this point, they become suspicious (stage two) and investigate the email by looking for technical details that can conclusively identify the email as phishing. Once they find such information, then they move to stage three and deal with the email by deleting it or reporting it. I discuss ways this process can fail, and implications for improving training of end users about phishing. 

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2. Detecting and Characterizing Lateral Phishing at Scale

   Ho, Grant; Cidon, Asaf; Gavish, Lior; Schweighauser, Marco; Paxson, Vern; Savage, Stefan; Voelker, Geoffrey M.; Wagner, David (August 2019, USENIX Security Symposium)

   We present the first large-scale characterization of lateral phishing attacks, based on a dataset of 113 million employee-sent emails from 92 enterprise organizations. In a lateral phishing attack, adversaries leverage a compromised enterprise account to send phishing emails to other users, benefitting from both the implicit trust and the information in the hijacked user's account. We develop a classifier that finds hundreds of real-world lateral phishing emails, while generating under four false positives per every one-million employeesent emails. Drawing on the attacks we detect, as well as a corpus of user-reported incidents, we quantify the scale of lateral phishing, identify several thematic content and recipient targeting strategies that attackers follow, illuminate two types of sophisticated behaviors that attackers exhibit, and estimate the success rate of these attacks. Collectively, these results expand our mental models of the `enterprise attacker' and shed light on the current state of enterprise phishing attacks 

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3. Knowledge and Capabilities that Non-Expert Users Bring to Phishing Detection

   Wash, Rick; Nthala, Norbert; Rader, Emilee (August 2021, Symposium on Usable Privacy and Security)

   null (Ed.)

   Phishing emails are scam communications that pretend to be something they are not in order to get people to take actions they otherwise would not. We surveyed a demographically matched sample of 297 people from across the United States and asked them to share their descriptions of a specific experience with a phishing email. Analyzing these experiences, we found that email users' experiences detecting phishing messages have many properties in common with how IT experts identify phishing. We also found that email users bring unique knowledge and valuable capabilities to this identification process that neither technical controls nor IT experts have. We suggest that targeting training toward how to use this uniqueness is likely to improve phishing prevention. 

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4. User Perceptions of Phishing Consequence Severity and Likelihood, and Implications for Warning Message Design

   https://doi.org/10.1007/978-3-030-79997-7_33  

   Eleanor K. Foster; Keith S. Jones; Miriam E. Armstrong; Akbar S. Namin (September 2021, International Conference on Applied Human Factors and Ergonomics)

   To combat phishing, system messages warn users of suspected phishing attacks. However, users do not always comply with warning messages. One reason for non-compliance is that warning messages contradict how users think about phishing threats. To increase compliance, warning messages should align with user perceptions of phishing threat risks. How users think about phishing threats is not yet known. To identify how users perceive phishing threats, participants were surveyed about their perceptions of the severity and likelihood of 9 phishing consequences. Results revealed perceived severity and likelihood levels for each consequence, as well as relative differences between consequences. Concrete examples of warning messages that reflect these findings are provided. 

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5. Detecting Phishing URLs using the BERT Transformer Model

   Otieno, D; Abri, F; Namin, A; Jones, K (December 2023, IEEE BigData)

   Phishing websites many a times look-alike to benign websites with the objective being to lure unsuspecting users to visit them. The visits at times may be driven through links in phishing emails, links from web pages as well as web search results. Although the precise motivations behind phishing websites may differ the common denominator lies in the fact that unsuspecting users are mostly required to take some action e.g., clicking on a desired Uniform Resource Locator (URL). To accurately identify phishing websites, the cybersecurity community has relied on a variety of approaches including blacklisting, heuristic techniques as well as content-based approaches among others. The identification techniques are every so often enhanced using an array of methods i.e., honeypots, features recognition, manual reporting, web-crawlers among others. Nevertheless, a number of phishing websites still escape detection either because they are not blacklisted, are too recent or were incorrectly evaluated. It is therefore imperative to enhance solutions that could mitigate phishing websites threats. In this study, the effectiveness of the Bidirectional Encoder Representations from Transformers (BERT) is investigated as a possible tool for detecting phishing URLs. The experimental results detail that the BERT transformer model achieves acceptable prediction results without requiring advanced URLs feature selection techniques or the involvement of a domain specialist. 

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