Malicious URL Detection Using Convolutional Neural Network

The World Wide Web has become an important part of our everyday life for information communication and knowledge dissemination. It helps to transact information timely, rapidly and easily. Identifying theft and identity fraud are referred as two sides of cyber-crime in which hackers and malicious users obtain the personal data of existing legitimate users to attempt fraud or deception motivation for financial gain. Malicious URLs host unsolicited content (spam, phishing, drive-by exploits, etc.) and lure unsuspecting users to become victims of scams (monetary loss, theft of private information, and malware installation), and cause losses of billions of dollars every year. To detect such crimes systems should be fast and precise with the ability to detect new malicious content. Traditionally, this detection is done mostly through the usage of blacklists. However, blacklists cannot be exhaustive, and lack the ability to detect newly generated malicious URLs. To improve the generality of malicious URL detectors, machine learning techniques have been explored with increasing attention in recent years. In this paper, I use a simple algorithm to detect and predicting URLs it is good or bad and compared with two other algorithms to know (SVM, LR).