Department Of Computer Science And Technology

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    A systematic literature review on phishing website detection techniques
    (King Saud bin Abdulaziz University, 2023-01-11T00:00:00) Safi, Asadullah; Singh, Satwinder
    Phishing is a fraud attempt in which an attacker acts as a trusted person or entity to obtain sensitive information from an internet user. In this Systematic Literature Survey (SLR), different phishing detection approaches, namely Lists Based, Visual Similarity, Heuristic, Machine Learning, and Deep Learning based techniques, are studied and compared. For this purpose, several algorithms, data sets, and techniques for phishing website detection are revealed with the proposed research questions. A systematic Literature survey was conducted on 80 scientific papers published in the last five years in research journals, conferences, leading workshops, the thesis of researchers, book chapters, and from high-rank websites. The work carried out in this study is an update in the previous systematic literature surveys with more focus on the latest trends in phishing detection techniques. This study enhances readers' understanding of different types of phishing website detection techniques, the data sets used, and the comparative performance of algorithms used. Machine Learning techniques have been applied the most, i.e., 57 as per studies, according to the SLR. In addition, the survey revealed that while gathering the data sets, researchers primarily accessed two sources: 53 studies accessed the PhishTank website (53 for the phishing data set) and 29 studies used Alexa's website for downloading legitimate data sets. Also, as per the literature survey, most studies used Machine Learning techniques; 31 used Random Forest Classifier. Finally, as per different studies, Convolution Neural Network (CNN) achieved the highest Accuracy, 99.98%, for detecting phishing websites. � 2023 The Author(s)
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    A comparative analysis and awareness survey of phishing detection tools
    (Institute of Electrical and Electronics Engineers Inc., 2018) Sharma, H.; Meenakshi, E.; Bhatia, S.K.
    Phishing is a kind of attack in which phishers use spoofed emails and malicious websites to steal personal information of people. Nowadays various tools are freely available to detect phishing and other web-based scams, many of which are browser extensions that generate a warning whenever user browses a suspected phishing site. In this research paper, comparison of eight phishing detection tools has been done to find the best one by testing each tool on the dataset, and further an awareness survey was carried out about these tools. Dataset contains two thousand verified phishing websites reported from August 2016 to March 2017 collected from two anti-phishing platforms i.e., Anti-Phishing Working Group (APWG) and PhishTank, and 500 legitimate websites that are visited by users regularly (i.e., Citibank.com, PayPal.com, Alibaba.com, Askfm.in, etc.) to test the effectiveness of eight popular anti-phishing tools. After testing all the tools on the dataset, it is found that AntiPhishing Toolbar did a very good job at identifying 94.32 percent of phishing as well as legitimate websites from the dataset. An awareness survey has been conducted among fifty M.tech Computer Science & Technology, and Cyber Security pursuing students at Central University of Punjab. The survey revealed that approximately 61 percent respondents are completely unaware about phishing detection tools. ? 2017 IEEE.