Identify malicious traffic on IoT infrastructure using neural networks and deep learning

The Internet of Things is a network of physical devices and equipment that includes sensors, software, and other technologies for exchanging data with other devices and systems over the Internet. The spread of the Internet of Things in the fields of smart health, smart agriculture, smart city, smart home, has revolutionized human life. Given the importance of the Internet of Things, identifying anomalies and malicious traffic is essential to maintaining privacy, network stability, and blocking unwanted behaviors. Due to the limited resources on IoT devices, traditional methods cannot be used directly to secure IoT devices and networks. To solve this problem, an artificial neural network-based identification method and in-depth learning has been developed to identify malformations and malicious traffic about which there is no predefined information. The data set used in this method is a combination of malicious and healthy traffic collected from related sources and feature extraction manually. Deep artificial neural network was applied to the data set and preprocessed and the results were analyzed with some conventional machine learning algorithms. The results show that the model designed using neural network and deep learning is able to detect anomalies and malicious traffic in the Internet of Things with an accuracy rate of more than 98.9% and an accuracy rate of 99.3%. In addition, the detection speed is 1.7 times faster than machine learning algorithms.