A REVIEW AND ANALYSIS OF BOT-IOT SECURITY DATA FOR MACHINE LEARNING

Machine learning is having an increased impact on the Cyber Security landscape. The ability for predictive models to accurately identify attack patterns in security data is set to overtake more traditional detection methods. Industry demand has led to an uptick in research in the application of machine learning for Cyber Security. To facilitate this research many datasets have been created and made public. This thesis provides an in-depth analysis of one of the newest datasets, Bot-IoT. The full dataset contains about 73 million instances (big data), 3 dependent features, and 43 independent features. The purpose of this thesis is to provide researchers with a foundational understanding of Bot-IoT, its development, its features, its composition, and its pitfalls. It will also summarize many of the published works that utilize Bot-IoT and will propose new areas of research based on the issues identified in the current research and in the dataset.