Open AccessRevisiting Recent and Current Anomaly Detection based on Machine Learning in Ad-Hoc Networks
Author(s) -
Zhixiao Wang,
Mingyu Chen,
Wenyao Yan,
Wendong Wang,
Ang Gao,
Gaoyang Nie,
Feng Wang,
Shaobo Yang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1288/1/012075
Subject(s) - intrusion detection system , computer science , wireless ad hoc network , anomaly detection , mobile ad hoc network , anomaly based intrusion detection system , intrusion prevention system , field (mathematics) , network security , anomaly (physics) , artificial intelligence , machine learning , computer network , data mining , wireless , network packet , telecommunications , mathematics , pure mathematics , physics , condensed matter physics
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.