FREMGANGSMÅDE OG SYSTEM TIL LAGDELT DETEKTERING AF PHISHING-WEBSITES

Phishing attacks cause financial frauds and credential thefts. The conventional blacklist, whitelist and Machine Learning (ML) based methods fail to provide an accurate detection of phishing attacks. The present disclosure provides a layered approach wherein a URL domain name is compared with blackl...

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Hauptverfasser: Tupsamudre, Harshal, LODHA, Sachin Premsukh
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creator Tupsamudre, Harshal
LODHA, Sachin Premsukh
description Phishing attacks cause financial frauds and credential thefts. The conventional blacklist, whitelist and Machine Learning (ML) based methods fail to provide an accurate detection of phishing attacks. The present disclosure provides a layered approach wherein a URL domain name is compared with blacklist domains and whitelist domains. Further, the URL undergoes Internet Protocol (IP) address checking followed by context checking. A clicked context is verified based on the number of search results from a popular search engine. Otherwise, the typed context is checked for non-ASCII characters in the domain name. Further, the URL is checked for any brand name. Further, the domain is checked for any misspelling. Further, the URL is examined using a Machine Learning (ML) model. Finally, the URL is classified as phishing if a number hits in a popular search engine is less. Here a phishing alert is generated in each layer based on the corresponding decision.
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The conventional blacklist, whitelist and Machine Learning (ML) based methods fail to provide an accurate detection of phishing attacks. The present disclosure provides a layered approach wherein a URL domain name is compared with blacklist domains and whitelist domains. Further, the URL undergoes Internet Protocol (IP) address checking followed by context checking. A clicked context is verified based on the number of search results from a popular search engine. Otherwise, the typed context is checked for non-ASCII characters in the domain name. Further, the URL is checked for any brand name. Further, the domain is checked for any misspelling. Further, the URL is examined using a Machine Learning (ML) model. Finally, the URL is classified as phishing if a number hits in a popular search engine is less. 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subjects CALCULATING
COMPUTING
COUNTING
ELECTRIC COMMUNICATION TECHNIQUE
ELECTRIC DIGITAL DATA PROCESSING
ELECTRICITY
PHYSICS
TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION
title FREMGANGSMÅDE OG SYSTEM TIL LAGDELT DETEKTERING AF PHISHING-WEBSITES
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