Abstract
Wireless IoT networks play a crucial role in shaping smart cities, facilitating efficient urban management and improving the quality of life for residents. These networks integrate various technologies to optimize infrastructure, energy consumption, and transportation systems while addressing challenges such as security and data management. Assessing the security of wireless IoT networks is critical due to their vulnerability to various cyber threats, including jamming and data breaches. Recent studies emphasize the importance of integrating advanced technologies such as machine learning and lightweight cryptography to enhance security measures. In turn, an attack graph can mimic the possible paths used by attackers to intrude into a network. The use of attack graph presents an opportunity to evaluate the security of the network and by analyzing one can also predict the behavior of the attacker. In the presented research, approaches to analyze the security of wireless IoT networks based on the graph model have been discussed. The main results of the study include the development of a risk assessment methodology, an algorithm for identifying attacker paths and the application of Markov model for predicting the network state. The work shows that the use of these methods allows for a deeper understanding of possible threats and the development of effective defense strategies.