Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/7686
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dc.contributor.authorRao, R.S.
dc.contributor.authorPais, A.R.
dc.date.accessioned2020-03-30T10:02:38Z-
dc.date.available2020-03-30T10:02:38Z-
dc.date.issued2017
dc.identifier.citationCPSS 2017 - Proceedings of the 3rd ACM Workshop on Cyber-Physical System Security, co-located with ASIA CCS 2017, 2017, Vol., , pp.33-42en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/7686-
dc.description.abstractIn this paper, we propose a technique to detect phishing attacks based on behavior of human when exposed to fake website. Some online users submit fake credentials to the login page before submitting their actual credentials. He/She observes the login status of the resulting page to check whether the website is fake or legitimate. We automate the same behavior with our application (FeedPhish) which feeds fake values into login page. If the web page logs in successfully, it is classified as phishing otherwise it undergoes further heuristic filtering. If the suspicious site passes through all heuristic filters then the website is classified as a legitimate site. As per the experimentation results, our application has achieved a true positive rate of 97.61%, true negative rate of 94.37% and overall accuracy of 96.38%. Our application neither demands third party services nor prior knowledge like web history, whitelist or blacklist of URLS. It is able to detect not only zero-day phishing attacks but also detects phishing sites which are hosted on compromised domains. � 2017 Copyright held by the owner/author(s).en_US
dc.titleDetecting phishing websites using automation of human behavioren_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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