Title: Detecting LDoS in NB-IoTs by using metaheuristic-based CNN

Authors: Chi-Yuan Chen; Hsin-Hung Cho; Min-Yan Tsai; Augustine Sii Ho Hann; Han-Chieh Chao

Addresses: Department of Computer Science and Information Engineering, National Ilan University, Yilan, Taiwan ' Department of Computer Science and Information Engineering, National Ilan University, Yilan, Taiwan ' Department of Computer Science and Information Engineering, National Ilan University, Yilan, Taiwan ' Department of Computer Science and Information Engineering, National Ilan University, Yilan, Taiwan ' National Dong Hwa University, Hualien, Taiwan

Abstract: The number of IoT devices will grow explosively due to great potential of the NB-IoT so that the IoT network environment will become a hotbed of botnets. Various IoT devices and platforms will be threatened by DoS attacks. However, NB-IoT has the characteristics of lower speed so it is not an ideal environment for heavy traffic DoS attacks. Low-rate DoS is the main attack method in the NB-IoT environment. In this environment, the attacker can hide the attack packet for avoiding detection in a data stream so that the difficulty of detection will be increased greatly. There are already many CNN methods to identify the characteristics of this attack. However, these traditional methods will cause the amount of data to be insufficiently diverse. In order to improve the phenomenon of overfitting, this article uses simulated annealing to adjust the weight of the CNN to achieve better global search.

Keywords: narrow band internet of things; NB-IoT; intrusion detection system; IDS; deep learning; heuristic algorithm.

DOI: 10.1504/IJAHUC.2021.115827

International Journal of Ad Hoc and Ubiquitous Computing, 2021 Vol.37 No.2, pp.74 - 84

Received: 17 Dec 2020
Accepted: 23 Dec 2020
Published online: 24 Jun 2021 *

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