ADAPTIVE FUZZY CLASSIFICATION-RULE ALGORITHM IN DETECTION MALICIOUS WEB SITES FROM SUSPICIOUS URLS

The Web has become a platform for supporting a wide range of criminal enterprises such as spam-advertised commerce, financial fraud and as a vector for propagating. Although the precise commercial motivations behind these schemes may differ, the common thread among them is the requirement that unsuspecting users visit their sites. These visits can be driven by email, Web search results or links from other Web pages, but all require the user to take some action, such as clicking, that specifies the desired Uniform Resource Locator (URL). Malicious Web sites are a cornerstone of Internet criminal activities. As a result, there has been broad interest in developing systems to prevent the end user from visiting such sites. In this paper we propose Classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The classification of new examples (antigens) considers not only the fitness of a fuzzy rule based on the entire training set, but also the affinity between the ride and the new example. This affinity must be greater than a threshold in order for the fuzzy rule to be activated. This proposed algorithm is considered to be an adaptive procedure for computing this threshold for each rule. Results are analyzed with respect to both predictive accuracy and rule set simplicity, and are compared with C4.5rulesy which is a very popular data mining classification algorithm