Deep Learning Solutions for Identifying and Preventing Web-based Malware

Malware that takes advantage of security holes is one type of threat that can affect web-based apps. The above breaches have the potential to seriously impair the privacy, usability, and security of the applications, putting both customers and businesses at risk. Finding out who is accessing web-based systems without permission is important for making them safer and lowering their weaknesses. The main topic of this piece is Deep Learning, a new way to find attacks on web-based software. It makes detection more accurate by combining the best parts of several machine learning methods. The main focus is on attacks that use security holes to target web-based apps. People are very likely to be affected by these acts. The CCCS_CIC_AndMal2022 dataset is used to test the method. The collection has many different types and characteristics that are all about how people use online apps. The collection makes it possible to train and test models well, which guarantees that they will work well in the real world. The results show that Deep Learning is very good at finding spying attempts that are aimed directly at web-based systems. This shows that it can make these applications much safer. It’s possible that this method could make web-based apps safer by making attack monitoring tools work better.