Research Article
OBNAI: An Outlier Detection-Based Framework for Supporting Network-Attack Identification over 5G Environment
@INPROCEEDINGS{10.1007/978-3-030-64214-3_5, author={Yi Shen and Yangfu Liu and Jiyuan Ren and Zhe Wang and Zhen Luo}, title={OBNAI: An Outlier Detection-Based Framework for Supporting Network-Attack Identification over 5G Environment}, proceedings={Mobile Computing, Applications, and Services. 11th EAI International Conference, MobiCASE 2020, Shanghai, China, September 12, 2020, Proceedings}, proceedings_a={MOBICASE}, year={2020}, month={12}, keywords={IP-Table Path selection ZB-Tree 5G}, doi={10.1007/978-3-030-64214-3_5} }- Yi Shen
Yangfu Liu
Jiyuan Ren
Zhe Wang
Zhen Luo
Year: 2020
OBNAI: An Outlier Detection-Based Framework for Supporting Network-Attack Identification over 5G Environment
MOBICASE
Springer
DOI: 10.1007/978-3-030-64214-3_5
Abstract
With the development of 5G, network attacking becomes more and more easy. Many system vulnerability are utilized to be attacked via 5G technology. It leads that the network attack frequency turn to high, and the network attack strength turns to strong. Among all network attack identification methods, outlier detection is one of the most important one. It aims to find data which is much different from most of the others. In this paper, we propose an outlier detection based framework to support network-attack identification. It first uses a novel algorithm to construct core point set so as support efficiently outlier detection. Next, it uses a novel index namedZB-Tree to manage these core points. Thirdly, we propose a predictive IP-table to handle and predict suspicious IP addresses. In this way, we could identify most suspicious IP addresses based on the position relationships among different base stations. Theoretical analysis and extensive experimental results demonstrate the effectiveness of the proposed algorithms.
