Prediction of malicious objects using prey-predator model in Internet of Things (IoT) for smart cities

•The study of malicious objects in the IoT network in smart cities.•Proposed models using the pre-predator model to detect malicious objects.•Stochastic behaviour of malicious objects in real dynamics is detected.•The study of proposed models in absence and presence of anti-malicious software is carried out. Presently, the interconnected networks in smart cities are increasing at a high pace using the Internet of Things (IoT), which allows the user to access the device from anywhere and gather the data. With such massive networks, data analysis plays a vital role in the real world to interpret and analyze the information, giving it a meaningful form. Such an extensive network of interconnected devices leads to a high probability of information theft and data alteration/manipulation through malicious attacks, leading to incorrect information floating over the network, resulting in misleading analysis. This may result is significant disaster in FOG computing environment resulting in failure of smart city applications like healthcare, smart traffic and many more. So, to over a model is required to identify the behaviors of attacker/malicious node. Many work are been proposed using clustering, game theory, fuzzy logic and Intrusion Detection System (IDS) to solve this but they do not consider the spread of infection affecting the other nodes. In this work a pre-predator model to determine the malicious node in the network has been proposed. The model consists prey and predator, where prey consists of infected nodes, whereas predators are malicious objects. In IoT networks, the delay is not the real factor in the spread of infection due to the chaotic nature of the malicious objects. So to overcome this issue, delay differential equation modeling is used. The performance of proposed model is compared with fuzzy logic and game theory based existing models taking into time to detect malicious, rate of infection and Count of infected node detected as performance parameters. Results shows 5% and 8% reduction in number of infected nodes as compared to fuzzy logic and game theory-based approach and reduction of 9% in infection rate.