Detection of malicious web contents using Machine and Deep Learning Approaches

ABSTRACT Websites have been the main target of intruders due to the fast progression of the Internet. An invader implants malicious content in a website page in order to perform a variety of bad and unwanted actions, such as stealing credentials and resources, tempting a web handler to an unsafe website, installing or downloading software to link a botnet, or participating in dispersed denial of service attacks. It can also damage user's system. Uninvited web content such as phishing, spam, and drive-by-downloads are hosted on malicious URLs, which entice unsuspecting users to become victims of schemes such as financial loss, data theft, and malware installation. Every year, billions of dollars are lost as a result of this. It is critical to detect and respond to such dangers as soon as possible. Keywords: Web content, URL, Cyber-crime, malware, Classification.