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

【Author】 Zeng, Jiang-Yi; Chang, Li-En; Cho, Hsin-Hung; Chen, Chi-Yuan; Chao, Han-Chieh; Yeh, Kuo-Hui

【Source】2022 5TH IEEE CONFERENCE ON DEPENDABLE AND SECURE COMPUTING (IEEE DSC 2022)

【影响因子】

【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.

【Keywords】Low-rate denial-of-service attacks; convolutional neural network; and Poisson distribution

【发表时间】2022

【收录时间】2023-05-31

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-物联网