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Vol. 8, Issue 1 (2019)

Phishing, machine, Safeguarding, models

Anshu Sharma
Since many of our regular activities-including financial transactions, work-related activities, and other daily activities-now take place online, we are more vulnerable to the dangers posed by cybercrime. Phishing attacks based on URLs are among the most frequent threats that internet user’s encounter. Rather than taking advantage of software bugs, the attacker in this kind of attack targets human weakness. The malicious software preys on people and institutions, deceiving them into opening secure websites, and either obtains private data or infects our computers with malware. Various machine learning algorithms are being used to identify phishing URLs, In order to distinguish between genuine and phishing URLs, researchers constantly strive to enhance the precision and overall efficiency of existing techniques. Our objective in this study is to validate various system learning approaches, as well as datasets and URL functionalities utilized to train these machine learning models. We examine and discuss the effectiveness of various machine learning algorithms as well as techniques for raising their accuracy metrics. Developing a survey resource to educate researchers about recent advancements in the field is the aim. They will be able to contribute to phishing detection models that produce outcomes that are more precise as a result.
Pages: 764-770  |  95 Views  42 Downloads

The Pharma Innovation Journal
How to cite this article:
Anshu Sharma. Phishing, machine, Safeguarding, models. Pharma Innovation 2019;8(1):764-770. DOI: 10.22271/tpi.2019.v8.i1m.25411

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