Deep learning model for cyber-attacks detection method in wireless sensor networks

Abstract

Nowadays, electronic applications are being adopted instead of many traditional processes in data and information management that use Internet technology as a transmission medium. Therefore, these data and information suffer from different types of attacks that aim to destroy or steal them. One of these attacks is the cyber classification that can halt the whole system. In this paper, a cyber-attacks detector method is proposed based on deep learning technology for Wireless Sensor Network (WSN). This method adopts the behavior of the WSN's nodes as well as the data transmission that depends on the MQTT protocol. The use of the deep learning model in this method improves the detection accuracy compared to traditional machine learning methods. The results demonstrate the efficiency of using the combination of deep learning CNN-LSTM techniques to be 96.02% in training accuracy and 95.08% for validation accuracy depending on the dataset of [1]. The machine learning model in [1] obtains an accuracy between 87% and 91% for the augmented dataset processes.