Trustworthiness testing of phishing websites: A behavior model-based approach

Shahriar, Hossain; Zulkernine, Mohammad

doi:10.1016/j.future.2011.02.001

Cited by 49 publications

(15 citation statements)

References 34 publications

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“…Real time 3 [118], [129], [148] 1 17 [98], [102], [106], [155]- [157], [121] [94], [99], [105], [132], [145]- [149], [ , [123], [140], [142] commonly used metrics, which are more suitable for balanced datasets. Among these two papers, only [136] actually used an unbalanced dataset.…”

Section: E Evaluation Metricsmentioning

confidence: 99%

SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective

Das

Baki

Aassal

et al. 2020

IEEE Commun. Surv. Tutorials

109

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Phishing and spear phishing are typical examples of masquerade attacks since trust is built up through impersonation for the attack to succeed. Given the prevalence of these attacks, considerable research has been conducted on these problems along multiple dimensions. We reexamine the existing research on phishing and spear phishing from the perspective of the unique needs of the security domain, which we call security challenges: real-time detection, active attacker, dataset quality and baserate fallacy. We explain these challenges and then survey the existing phishing/spear phishing solutions in their light. This viewpoint consolidates the literature and illuminates several opportunities for improving existing solutions. We organize the existing literature based on detection techniques for different attack vectors (e.g., URLs, websites, emails) along with studies on user awareness. For detection techniques we examine properties of the dataset, feature extraction, detection algorithms used, and performance evaluation metrics. This work can help guide the development of more effective defenses for phishing, spear phishing and email masquerade attacks of the future, as well as provide a framework for a thorough evaluation and comparison.

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Section: E Evaluation Metricsmentioning

confidence: 99%

SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective

Das

Baki

Aassal

et al. 2020

IEEE Commun. Surv. Tutorials

109

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“…Birhanu [66] proposed combining static analysis approaches that obtained features from URL and HTML content, and dynamic approaches that test the dynamics of a webpage during execution, such as proxy level, sandboxing, and honey clients with learning algorithms to detect phishing. Shahriar et al [67] proposed the creation of a finite state model to check the responses of websites to random inputs. Then, a set of heuristics was utilized to match this behavior with the behavior of phishing websites.…”

Section: Heuristics and Machine Learning-based Techniquesmentioning

confidence: 99%

A survey and classification of web phishing detection schemes

Varshney

Misra

Atrey

2016

Security Comm Networks

113

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Phishing is a fraudulent technique that is used over the Internet to deceive users with the goal of extracting their personal information such as username, passwords, credit card, and bank account information. The key to phishing is deception. Phishing uses email spoofing as its initial medium for deceptive communication followed by spoofed websites to obtain the needed information from the victims. Phishing was discovered in 1996, and today, it is one of the most severe cybercrimes faced by the Internet users. Researchers are working on the prevention, detection, and education of phishing attacks, but to date, there is no complete and accurate solution for thwarting them. This paper studies, analyzes, and classifies the most significant and novel strategies proposed in the area of phished website detection, and outlines their advantages and drawbacks. Furthermore, a detailed analysis of the latest schemes proposed by researchers in various subcategories is provided. The paper identifies advantages, drawbacks, and research gaps in the area of phishing website detection that can be worked upon in future research and developments. The analysis given in this paper will help academia and industries to identify the best anti-phishing technique.

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“…A behaviour-based model for trustworthiness testing was proposed in [24]. Instead of testing a website against a set of known inputs and comparing the expected outputs with the actual ones, the trustworthiness testing determines whether the response behaviour of a website matches our knowledge of phishing or legitimate website behaviours to decide whether a website is phishing or legitimate.…”

Section: B Recent Work On the Detection Of Phishingmentioning

confidence: 99%

Iterative Construction of Hierarchical Classifiers for Phishing Website Detection

Abawajy¹,

Beliakov²,

Kelarev³

et al. 2014

JNW

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This article is devoted to a new iterative construction of hierarchical classifiers in SimpleCLI for the detection of phishing websites. Our new construction of hierarchical systems creates ensembles of ensembles in SimpleCLI by iteratively linking a top-level ensemble to another middle-level ensemble instead of a base classifier so that the top-level ensemble can generate a large multilevel system. This new construction makes it easy to set up and run such large systems in SimpleCLI. The present article concentrates on the investigation of performance of the iterative construction of such classifiers for the example of detection of phishing websites. We carried out systematic experiments evaluating several essential ensemble techniques as well as more recent approaches and studying their performance as parts of the iterative construction of hierarchical classifiers. The results presented here demonstrate that the iterative construction of hierarchical classifiers performed better than the base classifiers and standard ensembles. This example of application to the classification of phishing websites shows that the new iterative construction combining diverse ensemble techniques into the iterative construction of hierarchical classifiers can be applied to increase the performance in situations where data can be processed on a large computer.

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Trustworthiness testing of phishing websites: A behavior model-based approach

Cited by 49 publications

References 34 publications

SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective

SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective

A survey and classification of web phishing detection schemes

Iterative Construction of Hierarchical Classifiers for Phishing Website Detection

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