Introduction​ In this project, we do the case study of detecting malicious URLs using machine learning in python.​

In the recent past, we have witnessed a significant increase in cybersecurity attacks such as ransomware, phishing, injection of malware, etc. on different websites all over the world. As a result of this, various financial institutions, e-commerce companies, and individuals incurred huge financial losses.​

In such type of scenario containing a cyber security attack is a major challenge for professionals & individuals as different types of new attacks are coming day by day.​

​What is URL?​ ​The Uniform Resource Locator (URL) is the well-defined structured format unique address for accessing websites over World Wide Web (WWW).​

​Dataset Description​ In this project, we will be using a Malicious URLs dataset of 6,51,191 URLs, out of which 4,28,103 benign or safe URLs, 96,457 defacement URLs, 94,111 phishing URLs, and 32,520 malware URLs.​ Now, let’s discuss different types of URLs in our dataset i.e., Benign, Malware, Phishing, and Defacement URLs.​ Benign URLs: These are safe to browse URLs.​ Example:- google.co.in, amazon.in, apple.com.​ Malware URLs: These type of URLs inject malware into the victim’s system once he/she visit such URLs.​ Example:- microencapsulation.readmyweather.com​ Defacement URLs: Defacement URLs are generally created by hackers with the intention of breaking into a web server and replacing the hosted website with one of their own, using techniques such as code injection, cross-site scripting, etc. Common targets of defacement URLs are religious websites, government websites, bank websites, and corporate websites.​ Phishing URLs: By creating phishing URLs, hackers try to steal sensitive personal or financial information such as login credentials, credit card numbers, internet banking details, etc.​ Example:-corporacionrossenditotours.com,http://drive-google-com.fanalav.com/6a7ec96d6a​

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