Evaluation of AI-based Malware Detection in IoT Network Traffic

Internet of Things (IoT) devices have become day-to-day technologies. They collect and share a large amount of data, including private data, and are an attractive target of potential attackers. On the other hand, machine learning has been used in several contexts to analyze and classify large volumes of data. Hence, using machine learning to classify network traffic data and identify anomalous traffic and potential attacks promises. In this work, we use deep and traditional machine learning to identify anomalous traffic in the IoT-23 dataset, which contains network traffic from real-world equipment. We apply feature selection and encoding techniques and expand the types of networks evaluated to improve existing results from the literature. We compare the performance of algorithms in binary classification, which separates normal from anomalous traffic, and in multiclass classification, which aims to identify the type of attack.