Enhanced IoT cybersecurity through Machine Learning - based penetration testing

The Internet of Things (IoT) is a new technology that builds on the old Internet. A network connects all objects using technologies such as Radio Frequency Identification (RFID), sensors, GPS, or Machine-to-Machine (M2M) communication. The development of IoT has been negatively impacted by security concerns, which has led to a significant increase in research interest. However, very few methods look at the security of IoT from the attacker's point of view. As of today, penetration testing is a common way to check the security of traditional internet or systems. It usually takes a lot of time and money. In this paper, we look at the security problems of the Internet of Things (IoT) and suggest a way to test for them. This way is based on a combination of the belief-desire intention (BDI) model and machine learning. The results of the experiments showed that they were very good at detecting and defending against cyberattacks on IoT devices. The proposed BDI-based recall method provided 85% of the results. The 90% precision suggests that the measurements are very accurate. The F1-score was 87.4%, and the accuracy was 95%. The proposed BDI is of exceptional quality in every part of the penetration-testing model.  Therefore, it is possible to create a system that can detect and defend against cyberattacks based on the proposed BDI model.