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PhishStorm: Detecting Phishing With Streaming Analytics
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PhishStorm: Detecting Phishing With Streaming Analytics
Abstract? Despite the growth of prevention techniques, phishing remains an important threat since the principal countermeasures in use are still based on reactive URL blacklisting. This technique is inef?cient due to the short lifetime of phishing Web sites, making recent approaches relying on real-time or proactive phishing URL detection techniques more appropriate. In this paper, we introduce PhishStorm, an automated phishing detection system that can analyze in real time any URL in order to identify potential phishing sites. Phish Storm can interface with any email server or HTTP proxy. We argue that phishing URLs usually have few relationships between the part of the URL that must be registered (low-level domain) and the remaining part of the URL < Final Year Projects 2016 > upper-level domain, path, query. We show in this paper that experimental evidence supports this observation and can be used to detect phishing sites. For this purpose, we de?ne the new concept of intra-URL relatedness and evaluate it using features extracted from words that compose a URL based on query data from Google and Yahoo search engines.
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| Domains | Datamining |
|---|---|
| Programming Language | Java |
