Searching Structural Neighborhood of Malicious URLs to Improve Blacklisting

Filtering based on blacklists is a major countermeasure against malicious websites. However, blacklists must be updated because malicious URLs tend to be short-lived and their sub strings may be partially mutated to avoid blacklisting. Due to these characteristics, it can be assumed that unknown malicious URLs exist in the neighborhood of known malicious URLs, created by the same adversary. We propose an effective blacklist URL generation method. We try to discover the URLs in the neighborhood of a malicious URL by using a search engine. Those suspicious neighborhoods around malicious URLs require further investigation to determine their blacklisting candidacy. We experimentally evaluated the proposed generation method by using real blacklisted URLs for both drive-by-download and click-download infection. The results showed that the proposed method can effectively improve identification of malicious URLs and maintenance of the coverage of blacklists.