Behind HumanBoost: Analysis of Users’ Trust Decision Patterns for Identifying Fraudulent Websites

Miyamoto, Daisuke; Hazeyama, Hiroaki; Kadobayashi, Youki; Takahashi, Takeshi

doi:10.4236/jilsa.2012.44033

Cited by 3 publications

(2 citation statements)

References 11 publications

(15 reference statements)

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“…This past trust decision is used as new heuristic and incorporate this with the eight existing heuristics of AdaBoost and proved that this improve the detection accuracy for web user. Daisue Miayamoto et al [22] have analyzed users' rust decision patterns for detecting phising sites. This work identify the type of users whose past trust decision is useful for detecting phising sites.…”

Section: Introductionmentioning

confidence: 99%

Efficient Harmful Email Identification Using Neural Network

Kathirvalavakumar

Kavitha

Palaniappan

2015

BJMCS

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Phishing is a form of online fraud that aims to steal a user's sensitive information such as online banking passwords or credit card numbers. In this paper, we present a technique to quickly detect suspected email using Neural Network Pruning approach. The goal is to determine whether the email is suspected or legitimate. A Multilayer feedforward neural network with Pruning Strategy is used for Feature Extraction and extracted features are used for identifying email as phishing email. Pruning Strategy extracts important features which are playing a key role in identifying phishing mail which looks similar to a legitimate one. To verify the feasibility of the proposed approach experimental evaluation has been performed using a dataset composed of phishing emails along with legitimate emails. The experimental results are satisfactory in terms of false positives and false negatives. The results of conducted test indicated good identification rate with very short processing time.

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Section: Introductionmentioning

confidence: 99%

Efficient Harmful Email Identification Using Neural Network

Kathirvalavakumar

Kavitha

Palaniappan

2015

BJMCS

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“…In our previous work [3], we asked 309 participants the reason of their decision. The participants browsed 14 simulated phishing sites and six legitimate sites, judging whether or not the site appeared to be a phishing site, and answered the reason for their decision via a questionnaire.…”

Section: Introductionmentioning

confidence: 99%

EyeBit: Eye-Tracking Approach for Enforcing Phishing Prevention Habits

Miyamoto

Iimura

Blanc

et al. 2014

2014 Third International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS)

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This paper proposes a cognitive method with the goal to get end users into the habit of checking the address bar of the web browser. Earlier surveys of end user behavior emphasized that users become victims to phishing due to the lack of knowledge about the structure of URLs, domain names, and security information. Therefore, there exist many approaches to improve the knowledge of end users. However, the knowledge gained will not be applied unless end users are aware of the importance and develop a habit to check the browser's address bar for the URL structure and relevant security information. We assume that the habit of checking the bar will improve educational effect, user awareness of secure information, and detection accuracy even in the case of sophisticated phishing attacks. To assess this assumption, this paper conducts a participant-based experiment where 23 participants' eye movement records are analyzed, and observes that novices do not tend to have the said habit. We then consider a way for them to acquire these habits, and develop a system which requires them to look at the address bar before entering some information into web input forms. Our prototype named EyeBit is developed as a browser extension, which interacts with an eye-tracking device to check if the user looks at the browser's address bar. The system deactivates all input forms of the websites, and reactivates them only if the user has looked at the bar. This paper shows the preliminary results of our participant-based experiments, and discusses the effectiveness of our proposal, while considering the potential inconvenience caused by EyeBit.

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Cognition in Social Engineering Empirical Research: A Systematic Literature Review

Burda,

Allodi,

Zannone

2024

ACM Trans. Comput.-Hum. Interact.

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The interdisciplinarity of the Social Engineering (SE) domain creates crucial challenges for the development and advancement of empirical SE research, making it particularly difficult to identify the space of open research questions that can be addressed empirically. This space encompasses questions on attack conditions, employed experimental methods, and interactions with underlying cognitive aspects. As a consequence, much potential in the breadth of existing empirical SE research and in its mapping to the actual cognitive processes it aims to measure is left untapped. In this work, we carry out a systematic review of 169 articles investigating overall 735 hypotheses in the field of empirical SE research, focusing on experimental characteristics and core cognitive features from both attacker and target perspectives. Our study reveals that experiments only partially reproduce real attacks and that the exploitable SE attack surface appears much larger than the coverage provided by the current body of research. Factors such as targets’ context and cognitive processes are often ignored or not explicitly considered in experimental designs. Similarly, the effects of different pretexts and varied targetization levels are overall marginally investigated. Our findings on current SE research dynamics provide insights into methodological shortcomings and help identify supplementary techniques that can open promising future research directions.

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Behind HumanBoost: Analysis of Users’ Trust Decision Patterns for Identifying Fraudulent Websites

Cited by 3 publications

References 11 publications

Efficient Harmful Email Identification Using Neural Network

Efficient Harmful Email Identification Using Neural Network

EyeBit: Eye-Tracking Approach for Enforcing Phishing Prevention Habits

Cognition in Social Engineering Empirical Research: A Systematic Literature Review

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