Phishing URL Detection Using URL Ranking

Feroz, Mohammed Nazim; Mengel, Susan

doi:10.1109/bigdatacongress.2015.97

Cited by 57 publications

(23 citation statements)

References 9 publications

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“…In the Appendix, we give tables with bibliographic numbers of the papers, so that interested readers can easily find the original sources for each entry in our tables in the main part of the paper. Accuracy 28 [21], [29], [30], [34]- [36], [42], [63], [64], [69], [80] [33], [44], [53], [55], [56], [66], [79], [86], [89] [22], [48], [50]- [52], [77], [81], […”

Section: Appendixmentioning

confidence: 99%

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

Das

Baki

Aassal

et al. 2020

IEEE Commun. Surv. Tutorials

101

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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: Appendixmentioning

confidence: 99%

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

Das

Baki

Aassal

et al. 2020

IEEE Commun. Surv. Tutorials

101

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“…Although the use of URL lexical features alone has been shown to result in high accuracy (∼97%) [29,30], phishers have learned how to make predicting a URL destination difficult by carefully manipulating the URL to evade detection [30]- [32]. Therefore, combining these features with others, such as host, is the most effective approach [33].…”

Section: A Url Lexical Featuresmentioning

confidence: 99%

“…It maintains information that is used as features, such as associated IP addresses (host, mail exchange, name server), Autonomous System number, domain name, sender policy framework, associated BGP and country code (e.g. [33,54,55,64]), IP address segments [49], time to live (TTL) [55,57,69,71], the number of resolved IP addresses [56,87], number of name server and the number of IPs name servers associated with [87]. Additionally, the ratio of malicious Autonomous System numbers for the resolved IP address and Name servers associated with the resolved IPs [87].…”

Section: B Host Featuresmentioning

confidence: 99%

A Review of Human- and Computer-Facing URL Phishing Features

Althobaiti

Rummani

Vaniea

2019

2019 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)

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When detecting phishing websites, both humans and computers rely on aspects of the website (features) to aid in their decision making. In this work, we conduct a review of URL-based phishing features that appear in publications targeting humanfacing and automated anti-phishing approaches. We focus on both humans and computers to obtain a more comprehensive feature list and create a cross-community foundation for future research. We reviewed 94 papers and categorise their features into: lexical, host, rank, redirection, certificate, search engine, and black/white lists. We find that research on automation has used all feature categories but several, such as host-based features (e.g. DNS), are minimally explored in human-facing anti-phishing research.

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“…Phishing attack is an online threat to the users that attempts to steal critical information and personal data through malicious websites. Such attack is performed by mimicking the legitimate websites and steal user data through registration forms, bank account details, chats, messages and further uses the stolen data to fraud users [8]. Common phishing attacks include link manipulation, website forgery and covert redirect.…”

Section: Introductionmentioning

confidence: 99%

Learning Model for Phishing Website Detection

Suryan¹,

Kumar²,

Mehta³

et al. 2018

ICST Transactions on Scalable Information Systems

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Website portal empowered with information technology are of great importance in present scenario. With access to data all around the world, securing our information becomes an issue of topmost priority. Over the decade there have been numerous attacks by phishing websites and people have lost huge resources. Such malicious websites, also known as phishing website, steal information of authenticate users and carry out illegal transactions by misusing the personal information. Phishing website links and associated e-mails are sent to billions of users daily, thereby becoming a big concern for cyber security. In this paper, we address the phishing problem using machine learning approach applied on our proposed model, which uses 30 distinct features for phishing detection. We extracted multiple features from the website link and applied appropriate algorithms to classify the link as legitimate or phishing links.

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Phishing URL Detection Using URL Ranking

Cited by 57 publications

References 9 publications

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

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

A Review of Human- and Computer-Facing URL Phishing Features

Learning Model for Phishing Website Detection

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