Quantifying Susceptibility to Spear Phishing in a High School
Environment Using Signal Detection Theory
- URL: http://arxiv.org/abs/2006.16380v2
- Date: Wed, 8 Jul 2020 12:02:05 GMT
- Title: Quantifying Susceptibility to Spear Phishing in a High School
Environment Using Signal Detection Theory
- Authors: Ploy Unchit, Sanchari Das, Andrew Kim, L. Jean Camp
- Abstract summary: Spear phishing is a deceptive attack that uses social engineering to obtain confidential information through targeted victimization.
Previous work on resilience to spear phishing has focused on convenience samples, with a disproportionate focus on students.
We engaged 57 high school students and faculty members as participants in research utilizing signal detection theory (SDT)
The results revealed an overconfidence bias in self-detection from the participants, regardless of their technical background.
- Score: 2.867517731896504
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Spear phishing is a deceptive attack that uses social engineering to obtain
confidential information through targeted victimization. It is distinguished by
its use of social cues and personalized information to target specific victims.
Previous work on resilience to spear phishing has focused on convenience
samples, with a disproportionate focus on students. In contrast, here, we
report on an evaluation of a high school community. We engaged 57 high school
students and faculty members (12 high school students, 45 staff members) as
participants in research utilizing signal detection theory (SDT). Through
scenario-based analysis, participants tasked with distinguishing phishing
emails from authentic emails. The results revealed an overconfidence bias in
self-detection from the participants, regardless of their technical background.
These findings are critical for evaluating the decision-making of
underrepresented populations and protecting people from potential spear
phishing attacks by examining human susceptibility.
Related papers
- A Quantitative Study of SMS Phishing Detection [0.0]
We conducted an online survey on smishing detection with 187 participants.
We presented them with 16 SMS screenshots and evaluated how different factors affect their decision making process in smishing detection.
We found that participants had more difficulty identifying real messages from fake ones, with an accuracy of 67.1% with fake messages and 43.6% with real messages.
arXiv Detail & Related papers (2023-11-12T17:56:42Z) - The Anatomy of Deception: Technical and Human Perspectives on a Large-scale Phishing Campaign [4.369550829556578]
This study takes an unprecedented deep dive into large-scale phishing campaigns aimed at Meta's users.
Analysing data from over 25,000 victims worldwide, we highlight the nuances of these campaigns.
Through the application of advanced computational techniques, including natural language processing and machine learning, this work unveils critical insights into the psyche of victims.
arXiv Detail & Related papers (2023-10-05T12:24:24Z) - Students Parrot Their Teachers: Membership Inference on Model
Distillation [54.392069096234074]
We study the privacy provided by knowledge distillation to both the teacher and student training sets.
Our attacks are strongest when student and teacher sets are similar, or when the attacker can poison the teacher set.
arXiv Detail & Related papers (2023-03-06T19:16:23Z) - An Overview of Phishing Victimization: Human Factors, Training and the
Role of Emotions [0.0]
Phishing is a form of cybercrime that allows criminals, phishers, to deceive end users in order to steal their confidential and sensitive information.
This paper explores the emotional factors that have been reported in previous studies to be significant in phishing victimization.
arXiv Detail & Related papers (2022-09-13T12:51:20Z) - A Tale of HodgeRank and Spectral Method: Target Attack Against Rank
Aggregation Is the Fixed Point of Adversarial Game [153.74942025516853]
The intrinsic vulnerability of the rank aggregation methods is not well studied in the literature.
In this paper, we focus on the purposeful adversary who desires to designate the aggregated results by modifying the pairwise data.
The effectiveness of the suggested target attack strategies is demonstrated by a series of toy simulations and several real-world data experiments.
arXiv Detail & Related papers (2022-09-13T05:59:02Z) - SoK: Human-Centered Phishing Susceptibility [4.794822439017277]
We propose a three-stage Phishing Susceptibility Model (PSM) for explaining how humans are involved in phishing detection and prevention.
This model reveals several research gaps that need to be addressed to improve users' detection performance.
arXiv Detail & Related papers (2022-02-16T07:26:53Z) - Deep convolutional forest: a dynamic deep ensemble approach for spam
detection in text [219.15486286590016]
This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically.
As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%.
arXiv Detail & Related papers (2021-10-10T17:19:37Z) - Falling for Phishing: An Empirical Investigation into People's Email
Response Behaviors [10.841507821036458]
Despite sophisticated phishing email detection systems, humans continue to be tricked by phishing emails.
We have carried out an empirical study to investigate people's thought processes when reading their emails.
We identify eleven factors that influence people's response decisions to both phishing and legitimate emails.
arXiv Detail & Related papers (2021-08-10T16:19:01Z) - Curse or Redemption? How Data Heterogeneity Affects the Robustness of
Federated Learning [51.15273664903583]
Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks.
This paper focuses on characterizing and understanding its impact on backdooring attacks in federated learning through comprehensive experiments using synthetic and the LEAF benchmarks.
arXiv Detail & Related papers (2021-02-01T06:06:21Z) - Sampling Attacks: Amplification of Membership Inference Attacks by
Repeated Queries [74.59376038272661]
We introduce sampling attack, a novel membership inference technique that unlike other standard membership adversaries is able to work under severe restriction of no access to scores of the victim model.
We show that a victim model that only publishes the labels is still susceptible to sampling attacks and the adversary can recover up to 100% of its performance.
For defense, we choose differential privacy in the form of gradient perturbation during the training of the victim model as well as output perturbation at prediction time.
arXiv Detail & Related papers (2020-09-01T12:54:54Z) - Phishing and Spear Phishing: examples in Cyber Espionage and techniques
to protect against them [91.3755431537592]
Phishing attacks have become the most used technique in the online scams, initiating more than 91% of cyberattacks, from 2012 onwards.
This study reviews how Phishing and Spear Phishing attacks are carried out by the phishers, through 5 steps which magnify the outcome.
arXiv Detail & Related papers (2020-05-31T18:10:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.