Towards a New Cyberdefense Generation: Proposition of an Intelligent Cybersecurity Framework for Malware Attacks

Objective: Newborn malware has increased significantly in recent years, becoming more dangerous for many applications. So, researchers are focusing more on solutions that serve the defense of new malware trends and variance, especially zero-day malware attacks. The prime goal of our proposition is to reach a high-security level by defending against malware attacks effectively using advanced techniques. Methods: In this paper, we propose an Intelligent Cybersecurity Framework specialized in malware attacks in a layered architecture. After receiving the unknown malware, the Framework Core layer uses malware visualization technique to process unknown samples of the malicious software. Then, we classify malware samples into their families using: K-Nearest Neighbor, Decision Tree, and Random Forest algorithms. Classification results are given in the last layer and based on a Malware Behavior Database; we are able to warn users by giving them a detailed report on the malicious behavior of the given malware family. The proposed Intelligent Cybersecurity Framework is implemented in a graphic user interface that is easy to use. Results: Comparing machine learning classifiers, the Random Forest algorithm gives the best results in the classification task with a precision of 97.6%. Conclusion: However, we need to take into account the results of the other classifiers for more reliability. Finally, obtained results are efficient and fast, meeting the cybersecurity frameworks' general requirements.