Revisiting Recent and Current Anomaly Detection based on Machine Learning in Ad-Hoc Networks
Year: 2019
Type: conference-paper
Abstract: Ad-Hoc network which is one kind of self-organized networks is much more vulnerable than the infrastructural network with the properties of highly changeable linkage, dynamic structure, and wireless connections so that the tradition intrusion detection system (IDS) should be improved to adapt in such network with limited computing resources and open channels. To ensure the security in Ad-Hoc network, the efficient anomaly detection methods should be probed. Over the past years, many studies have implemented anomaly detection methods (intrusion detection techniques) based on machine-learning methods in this field. This article analyzes the existing security problem in Ad-Hoc network, presents the basic theory of intrusion detection for Ad-Hoc network, and reviews the current and recent anomaly detection methods used machine learning techniques in the intrusion detection system.
Authors: Zhixiao Wang, Mingyu Chen, Wenyao Yan, Wendong Wang, Ang Gao, Gaoyang Nie, Feng Wang, Shaobo Yang
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Awards: G6763520804, G7744406857, 基于对象的监控视频检索方法研究
OpenAlex ID: W2967701409
