Malware Detection Using Ensemble Learning and File Monitoring

In essence, malware refers to harmful programs that cybercriminals use to infiltrate a specific machine or an organisation’s complete network. It takes advantage of flaws in legitimate software (such a browser or plugin for an online application) that can be hijacked. ML is widely used to mitigate this problem which is an excellent solution but the problem with this is that it’s possible for ML to falsely detect some files causing system exploits. This paper aims to provide a method to detect malware using ensemble learning and further monitor files based on a probability value assigned to it by the model.