In recent years, the spread of malware has increased exponentially, posing a significant challenge for cybersecurity experts. When facing with the constantly evolving world of unknown threats, including zero-day attacks, traditional signature-based approaches for malware detection have proven to be insufficient. Furthermore, adversaries are adapting by modifying their malicious code, which reduces the efficacy of signature-based detection. As a solution to these problems, machine learning models have been used to develop behaviour-based malware detection systems, because of their ability to generalise from data and detect previously unseen malware. These systems are employed to inspect the code in order to identify any malicious or potentially harmful actions performed by that code.