Results 151 to 160 of about 2,777,664 (208)

Ransomware attacks and cybersecurity concerns in modern hospitals: vulnerabilities and impacts on trauma centers and patient care. [PDF]

Trauma Surg Acute Care Open
Martin MJ, Patel PP, Egodage T.
europepmc   +1 more source

Lightweight malicious URL detection using deep learning and large language models. [PDF]

Sci Rep
Kibriya H, Amin R, Alshamrani SS, Rehman S, Hassan M, Alsubaei FS.   +5 more
europepmc   +1 more source

Editorial: Machine learning for cybersecurity. [PDF]

Front Artif Intell
Wickramasinghe Brahmana CS, Marino D, De Silva D, Manic M.   +3 more
europepmc   +1 more source

Mapping Machine Learning-Driven Cybersecurity Solutions in Health Care: Scoping Literature Review. [PDF]

J Med Internet Res
Rajput K, Zuberi S, Elhajj M, Ochieng W, Darzi A, Ghafur S.   +5 more
europepmc   +1 more source

Revamping Password Security: Leveraging Mnemonics for Enhanced Authentication. [PDF]

Int J Inf Secur
Adjei HAS, Giovanetti D, Vargas Martin M, Dell'Amico M, Di Prodi P.   +4 more
europepmc   +1 more source

Football cybersecurity threat severity prediction using multi-head transformer-based deep learning models. [PDF]

Sci Rep
Hassan BM, Algarni F, Alshamrani R, Althbiti A, Albalawi A, Ismail A.   +5 more
europepmc   +1 more source

Phishing URL Detection

International Journal for Research in Applied Science and Engineering Technology
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Phishing Detection: A Literature Survey

IEEE Communications Surveys and Tutorials, 2013
This article surveys the literature on the detection of phishing attacks. Phishing attacks target vulnerabilities that exist in systems due to the human factor. Many cyber attacks are spread via mechanisms that exploit weaknesses found in end-users, which makes users the weakest element in the security chain. The phishing problem is broad and no single
Youssef Iraqi, Andrew Jones
exaly   +3 more sources

Phishing Detection

Applied Computer Science and Software Engineering, 2019
Phishing attacks have become a serious threat in the realm of cybersecurity, with both direct and indirect impacts on individuals and organizations. This study aims to assess the performance of three machine learning classification algorithms—Decision Tree, Random Forest, and Support Vector Machine—in detecting phishing attempts through the use of ...
Bahriddin Abapihi, Dewi Fortuna
core   +4 more sources

An Intelligent Anti-phishing Strategy Model for Phishing Website Detection

2012 32nd International Conference on Distributed Computing Systems Workshops, 2012
As a new form of malicious software, phishing websites appear frequently in recent years, which cause great harm to online financial services and data security. In this paper, we design and implement an intelligent model for detecting phishing websites.
Weiwei Zhuang, Qingshan Jiang, Tengke Xiong   +2 more
core   +4 more sources