IEWS: a Free Open Source Intelligent Early Warning System Based on Machine Learning

With the introduction of new technologies and numerous services provided in the framework of computer networks, as well as the growth in malicious software that is abusing the Internet on a regular basis, the Internet has become a severe threat. In particular, malicious software called Malware, such as viruses, ransomware and spyware, become a worldwide epidemic, and studies indicate that the effect is worsening. Many approaches have been presented so far to cope with these many dangers. All of these tactics have the same purpose in mind: to prevent attackers from achieving their non-innocent goals. Without a doubt, manual heuristic Malware analysis is no longer deemed useful and efficient in light of the rapid pace of Malware dissemination. Therefore, automated behaviour based Malware detection using advanced machine learning techniques is considered a profound solution. In this study, we propose an Open Source free Intelligent Early Warning System (IEWS) for the autonomous analysis of Malware behaviour using a machine learning algorithm. The proposed system enables the early and fast identification of Malware before it’s into the system as a way to protect the network environment from a malicious activity. Results show that a network equipped with our proposed IEWS has the ability to identify possible malware attacks with a detection accuracy of 99.9%.