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A proactive discovery and filtering solution on phishing websites

2015 IEEE International Conference on Big Data (Big Data), 2015
Phishing website is becoming a major threat to the information security in Social Network. The attacks not only lessen the users' trust but also influence the benefit of the third party who develops the platform. In order to solve the time lag in phishing website passive detection, this paper proposes a solution to discover phishing website ...
Fang Lv   +4 more
openaire   +1 more source

User behaviour based phishing websites detection

2008 International Multiconference on Computer Science and Information Technology, 2008
Phishing detection systems are principally based on the analysis of data moving from phishers to victims. In this paper we describe a novel approach to detect phishing websites based on analysis of userspsila online behaviours - i.e., the websites users have visited, and the data users have submitted to those websites.
Xun Dong, John A. Clark, Jeremy L. Jacob
openaire   +1 more source

Evaluating the Wisdom of Crowds in Assessing Phishing Websites

2008
We examine the structure and outcomes of user participation in PhishTank, a phishing-report collator. Anyone who wishes may submit URLs of suspected phishing websites, and may vote on the accuracy of other submissions. We find that PhishTank is dominated by its most active users, and that participation follows a power-law distribution, and that this ...
Tyler Moore 0001, Richard Clayton 0001
openaire   +1 more source

Improving the classification of phishing websites using a hybrid algorithm

Computational Intelligence, 2022
Suvita Rani Sharma   +2 more
exaly  

Phishing websites detection using a novel multipurpose dataset and web technologies features

Expert Systems With Applications, 2022
Manuel Sánchez-Paniagua   +2 more
exaly  

Identification of phishing websites through hyperlink analysis and rule extraction

Electronic Library, 2020
Zhongyi Hu, Raymond Chiong, Yukun Bao
exaly  

Phishing Website Detection and Classification

2022
D. Viji, Vaibhav Dixit, Vishal Jha
openaire   +1 more source

A domain-feature enhanced classification model for the detection of Chinese phishing e-Business websites

Information and Management, 2014
Dongsong Zhang, Zhijun Yan, Taeha Kim
exaly  

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