Results 11 to 20 of about 7,019 (217)
Phishing to improve detection [PDF]
Proceedings of the 2023 European Symposium on Usable Security, 2023 Phishing e-mail scams continue to threaten organisations around the world. With generative artificial intelligence, conventional phishing detection advice such as looking out for linguistic errors and bad layouts will become obsolete.
Sarah Ying Zheng, Ingolf Becker
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Improving the phishing website detection using empirical analysis of Function Tree and its variants
Heliyon, 2021 The phishing attack is one of the most complex threats that have put internet users and legitimate web resource owners at risk. The recent rise in the number of phishing attacks has instilled distrust in legitimate internet users, making them feel less ...
Abdullateef O. Balogun +10 more
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PhiKitA: Phishing Kit Attacks Dataset for Phishing Websites Identification
IEEE Access, 2023 Recent studies have shown that phishers are using phishing kits to deploy phishing attacks faster, easier and more massive. Detecting phishing kits in deployed websites might help to detect phishing campaigns earlier.
Felipe Castano +3 more
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Learning to detect phishing emails [PDF]
Proceedings of the 16th international conference on World Wide Web, 2006 Each month, more attacks are launched with the aim of making web users believe that they are communicating with a trusted entity for the purpose of stealing account information, logon credentials, and identity information in general. This attack method, commonly known as "phishing," is most commonly initiated by sending out emails with links to spoofed
Ian Fette +2 more
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Business Email Compromise Phishing Detection Based on Machine Learning: A Systematic Literature Review [PDF]
, 2022 The risk of cyberattacks against businesses has risen considerably, with Business Email Compromise (BEC) schemes taking the lead as one of the most common phishing attack methods.
Hany F. Atlam +4 more
core +1 more source
PDGAN: Phishing Detection With Generative Adversarial Networks
IEEE Access, 2022 Phishing is a harmful online attack that could lead to identity theft and financial damages. The demand for high-accuracy phishing detection tools has risen due to the increase of online electronic services and payment systems.
Saad Al-Ahmadi +2 more
doaj +1 more source
International Journal of Networked and Distributed Computing (IJNDC), 2020 This article presents the threshold-based incremental learning model for a case-base updating approach that can support adaptive detection and incremental learning of Case-based Reasoning (CBR)-based automatic adaptable phishing detection.
San Kyaw Zaw, Sangsuree Vasupongayya
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SPWalk: Similar Property Oriented Feature Learning for Phishing Detection
IEEE Access, 2020 Detecting phishing webpages is an essential task that protects legitimate websites and their users from various malicious activities. To classify the suspect webpage as phishing or legitimate, robust and effective features used for classification are in ...
Xiuwen Liu, Jianming Fu
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Overconfidence in Phishing Email Detection
Journal of the Association for Information Systems, 2016 This study examines overconfidence in phishing email detection. Researchers believe that overconfidence (i.e., where one’s judgmental confidence exceeds one’s actual performance in decision making) can lead to one’s adopting risky behavior in uncertain ...
Jingguo Wang +2 more
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Detecting Phishing Sites -- An Overview
CoRR, 2021 Phishing is one of the most severe cyber-attacks where researchers are interested to find a solution. In phishing, attackers lure end-users and steal their personal in-formation. To minimize the damage caused by phishing must be detected as early as possible.
P. Kalaharsha, Babu M. Mehtre
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