Detection and classification malicious URLS on social media using machine learning

Recently, the variety and size of malware on the social networks has increased dramatically, bearing phrases and headings aimed at attracting attention and pushing them to enter the link that contains malicious software, causing theft (bank accounts, financial transactions, installing malicious software) and therefore it is necessary to discover These risks and threats are addressed. The purpose of this thesis is to discover malware and classify it into benign or malicious URLs using a machine learning algorithm called Support vector machine, which is used in binary classification and the algorithm was utilized for creating a model for malware detection. This study is conducting comprehensive experiments for the purpose of comparing and verifying the suggested method's results with the ones of other techniques. The experimental results are showing that the presented approach is achieving strong detection and high accuracy of up to 93%, and it is a method that achieves strong detection compared to other results for detecting malware.