A Review and Analysis of the Bot-IoT Dataset

Machine learning is rapidly changing the cybersecurity landscape. The use of predictive models to detect malicious activity and identify inscrutable attack patterns is providing levels of automation that are desperately needed to level the playing field between malicious actors and network defenders. This has led to increased research at the intersection of machine learning and cybersecurity and also the creation of many publicly available datasets. This paper provides an in-depth, unique review and analysis of one of the newest datasets, Bot-IoT. The full dataset contains about 73 million instances (big data). Models trained on Bot-IoT are capable of detecting various botnet attacks in Internet of Things (IoT) networks. The purpose of this paper is to provide researchers with a fundamental understanding of Bot-IoT, its features, and some of its pitfalls. We also discuss data cleaning procedures and briefly summarize the use of the dataset in published research.