Abstract
Cyberspace is a virtual environment where communication over computer networks happen. This space is vulnerable to cyber-attacks. After the attack happened, it has left many questions. The most important one is "who did it?". In order to improve cyber-attribution process Machine Learning can be used, and that is what has been done in this study. The proposed model is built based on Amazon Web Services (AWS) Honeypot dataset. Five models were built using five techniques: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Naive Bayesian (NB) and Bayesian Network (BN). This study builds machine learning based cyber attribution models that are able to effectively aid analysts in attributing a cyber-attack appropriately and accurately. Experimental results indicated that the SVM model achieved the best accuracy, among others, which is 94.79%.