Top-Down Machine Learning-Based Architecture for Cyberattacks Identification and Classification in IoT Communication Networks

Abstract

With the prompt revolution and emergence of smart, self-reliant, and low-power devices, Internet of Things (IoT) has inconceivably expanded and impacted almost every real-life application. Nowadays, for example, machines and devices are now fully reliant on computer control and, instead, they have their own programmable interfaces, such as cars, unmanned aerial vehicles (UAVs), and medical devices. With this increased use of IoT, attack capabilities have increased in response, which became imperative that new methods for securing these systems be developed to detect attacks launched against IoT devices and gateways. These attacks are usually aimed at accessing, changing, or destroying sensitive information; extorting money from users; or interrupting normal business processes. In this research, we present new efficient and generic top-down architecture for intrusion detection, and classification in IoT networks using non-traditional machine learning is proposed in this article. The proposed architecture can be customized and used for intrusion detection/classification incorporating any IoT cyber-attack datasets, such as CICIDS Dataset, MQTT dataset, and others. Specifically, the proposed system is composed of three subsystems: feature engineering (FE) subsystem, feature learning (FL) subsystem, and detection and classification (DC) subsystem. All subsystems have been thoroughly described and analyzed in this article. Accordingly, the proposed architecture employs deep learning models to enable the detection of slightly mutated attacks of IoT networking with high detection/classification accuracy for the IoT traffic obtained from either real-time system or a pre-collected dataset. Since this work employs the system engineering (SE) techniques, the machine learning technology, the cybersecurity of IoT systems field, and the collective corporation of the three fields have successfully yielded a systematic engineered system that can be implemented with high-performance trajectories.

Keywords: IoT communications; convolutional neural network; cyberattacks detection; deep neural network; machine learning; shallow neural network; systems engineering.