The World Wide Web has become a huge part of millions of people who use online services e.g. net banking, net shopping, social networking, e-commerce, and store and manage user sensitive information, etc. In fact, it is a popular tool for all user over the Internet. Rich Web based applications are available over the World Wide Web to provide all types of services. At the same time, the Web has become an important means for people to interact with each other and so on. This is the positive side of this technology. Unfortunately, the Web has also become a more dangerous technique. The popularity of World Wide Web has also attracted obtrudes and attackers. These obtrudes abuse the Internet and users by performing illegal activity for financial profit. The Web pages that contain such types of attacks or malicious code are called as malicious Web pages or malware. While the existing system are good sign to detecting malicious Web pages, there are still open issues in Web page features extraction and detection techniques. In this paper, we are detecting and identified malicious or benign URL classification using machine learning in real time.

URLs, Detection, Malicious Webpages, Machine learning.

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