Malware Detection and Prediction System Using Advanced Machine Learning Algorithms

Abstract

Abstract: Given how many websites are now disseminating malware, malicious software is one of the biggest hazards in today's digital environment. Computer systems connected to the Internet increasingly require malware analysis and protection techniques. Without the user's knowledge, malicious software takes advantage of system flaws to steal important data and covertly send it to distant servers under the control of attackers. Static or dynamic analysis methods are used to analyse malware. These methods categorise and forecast distinctive patterns to effectively detect malware. Software assaults have the potential to corrupt or harm devices, compromise entire systems, steal information, change data, and deny service. The detection and prediction system is implemented as a user-friendly website with a variety of admin and user-facing modules. The file that requires detection and prediction can be uploaded by the admin user. If there are any API calls used in the file with malware purpose, the system will examine them. This API can provide security dangers to the computer because it was designed with malware in mind; as a result, it must be found and immediately removed to prevent security difficulties. The system that was built can determine the sort of malware that is encoded in the file and can assist users in determining the threat that such malware may pose to their computer.