Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Characteristics that Predict Phishing Susceptibility: A Review
Phishing attack countermeasures have previously relied on technical solutions or user training. As phishing attacks continue to impact users resulting in adverse consequences, mitigation efforts may be strengthened through an understanding of how user characteristics predict phishing susceptibility. Several studies have identified factors of interest that may contribute to susceptibility. Others have begun to build predictive models to better understand the relationships among factors in addition to their prediction power, although these studies have only used a handful of predictors. As a step toward creating a holistic model to predict phishing susceptibility, it was first necessary to catalog all known predictors that have been identified in the literature. We identified 32 predictors related to personality traits, demographics, educational background, cybersecurity experience and beliefs, platform experience, email behaviors, and work commitment style.  more » « less
Award ID(s):
1723765
PAR ID:
10350789
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Volume:
65
Issue:
1
ISSN:
2169-5067
Page Range / eLocation ID:
938 to 942
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; (, 2022 IEEE 30th International Requirements Engineering Conference Workshops (REW))
    Abstract—Many organizations use internal phishing campaigns to gauge awareness and coordinate training efforts based on those findings. Ongoing content design is important for phishing training tools due to the influence recency has on phishing susceptibility. Traditional approaches for content development require significant investment and can be prohibitively costly, especially during the requirements engineering phase of software development and for applications that are constantly evolving. While prior research primarily depends upon already known phishing cues curated by experts, our project, Phish Finders, uses crowdsourcing to explore phishing cues through the unique perspectives and thought processes of everyday users in a realistic yet safe online environment, Zooniverse. This paper contributes qualitative analysis of crowdsourced comments that identifies novel cues, such as formatting and typography, which were identified by the crowd as potential phishing indicators. The paper also shows that crowdsourcing may have the potential to scale as a requirements engineering approach to meet the needs of content labeling for improved training tool development. 
    more » « less
  2. ; ; ; (, Proceedings of the ACM on Human-Computer Interaction)
    Social media have become essential in daily life, serving as platforms for public opinion, personal growth, and news consumption. This shift in how people access news has led to an increase in misinformation, including fake news intended to deceive. Various psychological and social factors, such as emotional appeal, cognitive biases, and social influence, drive individuals' susceptibility to believing false information. While prior studies have examined the believability of news content, large-scale analyses on real social media platforms remain limited, as well as studying factors influencing the believability of real and fake news separately and analyzing similarities and differences. This study introduces a new dataset of 14,535 Twitter user comments, annotated to measure user believability in real versus fake news. Using this dataset, we address the problem of predicting news believability and study which user or news characteristics predict believability in real and fake news, as well as checking whether believability enhances fake news detection. We employ machine learning models incorporating news style, emotional content, and user traits to predict believability and apply explainability methods to clarify key characteristics influencing user belief. Overall, the models achieved significant results in detecting news believability and several news and user-based features such as writing style, emotion, personality, and psychology have been individuated as strong predictors of believability in news. We further integrate believability insights into advanced fake news detectors, demonstrating improved performance. To our knowledge, this is the first large-scale human-annotated English dataset designed for studying news believability, which we have released for use in future research. 
    more » « less
  3. ; (, User Modeling and User-Adapted Interaction)
    Phishing attacks remain a significant security threat. One approach to addressing this challenge is through personalized and adaptive anti-phishing training solutions capable of tailoring learning experiences to individual needs and context. This requires cognitive models that are predictive of individual phishing responses and are amenable to analyzing and measuring the cognitive factors underlying people's susceptibility to phishing attacks. In this paper, we study a key challenge associated with developing cognitive models of phishing decision making grounded in instance based learning theory (IBLT): instance engineering. We investigate the effectiveness of different approaches to designing instances using transformer-based methods for natural language representation. This work also investigates which aspects of phishing decision making IBL models could represent and predict. We found that using representations that consider contextual meanings assigned by humans could enable cognitive agents to predict human responses to phishing emails with high accuracy. In particular, we also found that the IBL models were predictive of the responses of participants who participated in the quick and intuitive form of the decision-making process. This work underscores cognitive models' potential to analyze differences in individual's responses to phishing attacks, identify gaps in security awareness, and enhance anti-phishing training effectiveness. 
    more » « less
  4. ; (, AHFE International)
    Phishing and spear-phishing remain among the most persistent cybersecurity threats. This study examines end-user decision-making in spear-phishing contexts by modeling the relationship between visual attention and responses using eye-tracking measures. Forty-eight university students completed an email management task while eye movements were recorded with a Tobii Nano Pro tracker. Participants classified 50 emails (phishing, spear-phishing, promotional, legitimate) drawn from a corpus of 481 messages. Cognitive load was manipulated via concurrent puzzle solving and validated using NASA-TLX. To minimize bias, participants assumed fictional personas and made realistic decisions without explicit phishing instructions. Cognitive load and fatigue showed no significant effect on phishing susceptibility, though later trials exhibited a marginal increase in response bias. In contrast, eye-tracking metrics strongly predicted decisions. Logistic regression revealed that longer first fixation durations and higher saccade counts increased likelihood of responding, while larger pupil diameters were negatively associated with responses. Beyond results from the experiment, the study proposes attention-based representations integrating eye-tracking with natural language processing to improve cognitive models. 
    more » « less
  5. ; (, AHFE International)
    Risk arising from human behavior, such as employees falling victim to phishing, continues to undermine organizational security posture. Prior work has attributed phishing susceptibility to attentional failures in detecting suspicious cues, motivating training approaches focused on detecting such cues. However, growing evidence suggests that susceptibility to phishing is better explained through activation and retrieval process of relevant experiences from memory. Models capable of estimating awareness gaps and predicting how individuals respond to or report phishing emails are therefore critical for delivering personalized training and testing interventions. A key challenge in building such cognitive models is finding effective ways to represent the contextual cues that shape how individuals perceive, store, and recall phishing related content. This paper applies a privileged learning strategy to construct richer instance representations within cognitive models of phishing judgment. Combining instance-based learning (IBL) with neural network-based text similarity, we infer how recipients interpret email content and underlying intent. Results indicate that this privileged learning pipeline substantially enhances the predictive ability of cognitive models of phishing, opening new methods for developing individualized anti-phishing interventions. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Text Encoded Conversion
  • XML
  • Save / Share this Record
  • Send to Email