Using Neural Network Techniques to Detect Malicious URL for Preventing Ransomwares

Abstract

Abstract: Ransom ware is a kind of malignant malware programming that takes steps to distribute or hinders admittance to information or a PC framework, for the most part by scrambling it, until the casualty pays a payoff expense to the assailant. As a rule, the payoff request accompanies a cutoff time. Presently days and assailants executed new strategies for effective working of assault. The World Wide Web has become an important part of our everyday life for information communication and knowledge dissemination. 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. The current work focuses on developing a model using convolutional neural networks for efficiently preventing ransom wares.