Malicious URL Detection Using Machine Learning

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dc.contributor.authorPRASHANTH, MANOHAR G
dc.contributor.authorPUNITH KAMINI, S
dc.contributor.authorMAHENDRA, V
dc.contributor.authorBHASKAR, M
dc.date.accessioned2018-06-19T09:56:11Z
dc.date.available2018-06-19T09:56:11Z
dc.date.issued2018-06-19T09:56:11Z
dc.description.abstractNew Communication Techmologies has had a tremendous impact in promotion and business growth spanning across many applications. The importance of World Wide Web has continuously been increasing. Unfortunately, the technological advancements come with new sophisticated techniques to attack and scam users. Such attacks include malicious websites that sell counterfeit goods, revealing sensitive information which eventually lead to theft of money leading to financial fraud and theft of money or identity occurs, they can even install malware in the user’s system. There are wide variety of implementations for the attacks such as explicit hacking attempts, drive-by exploits phishing, watering hole, Social engineering, man-in-the middle, SQL injections, loss or theft services etcen_US
dc.identifier.urihttp://hdl.handle.net/123456789/9432
dc.language.isoenen_US
dc.subjectPRASHANTH MANOHAR Gen_US
dc.subjectPUNITH KAMINI Sen_US
dc.subjectMAHENDRA Ven_US
dc.subjectBHASKAR Men_US
dc.subject1NH14CS093en_US
dc.subject1NH14CS096en_US
dc.subject1NH14CS065en_US
dc.subject(1NH15CS424en_US
dc.titleMalicious URL Detection Using Machine Learningen_US
dc.typeOtheren_US
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