Using Poisson Distribution to Enhance CNN-based NB-IoT LDoS Attack Detection

Abstract

Because the hardware capabilities of narrowband IoT devices are not enough to carry powerful antivirus software or security mechanisms so that some scholars have used deep learning to help with intrusion detection. Narrowband IoT devices are more vulnerable to low-rate denial-of-service attacks due to the low upper limit of the connection rate. However, the rate and number of such attacks are not obvious. Therefore, even when training with datasets provided by large organizations, the amount of data for low-rate denial-of-service attacks is very sparse, resulting in poor detection accuracy. This study proposes an interpretable method based on statistical models to simplify the model so that it responds only to specific attacks. The experimental results show that our method can effectively detect specific attacks.