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In the era of internet, users are keen to discover more in the web. As the number of web pages increases day-by-day malicious web pages are also increasing proportionally. This paper focus on detecting maliciousness in a web page using genetically evolved fuzzy rules. The above formed rules are filtered by Support Vector Machine and finally storing the result in a symbolic knowledge base, with appropriate weightage for each rule. This provides an insight to symbolic and non-symbolic intelligence in malicious web page detection. General Terms Malicious web page, Static features, Potential features, Rule weightage.

Predicate based Algorithm for Malicious Web Page Detection using Genetic Fuzzy Systems and Support Vector Machine