Attack Detection in IoT using RF and ANN
Aigul Adamova, Tamara Zhukabayeva, Nurgalym Adamov · 2024
Internet of Things (IoT) devices are the weak link in organizing a Wireless Sensor Network. Various Attacks on IoT devices can lead to different complex consequences. Real applications of the IoT generate a large amount of data every second, the confidentiality of which is of very high value. Therefore, detecting attacks in IoT interactions is of paramount interest from both science and industry. Among various attack detection approaches, machine learning methods show great potential due to their early detection ability. The paper presents a methodology for detecting attacks based on two machine learning methods: Random Forest (RF) and Artificial Neural Network (ANN). To conduct the experiment, various data sets were considered, the descriptions of which are given in the work. The experiment was carried out using open data sets obtained from real IoT devices. As a result, RF demonstrated a high accuracy of 98.6%.