Intrusion detection method for mobile ad-hoc networks based on machine learning
基于机器学习的移动自组织网络入侵检测方法

Mobile ad-hoc networks(MANETs) represent complex distributed communication systems comprised of wireless mobile nodes.Based on the discussion of intrusion detection problem in MANET,a novel anomaly intrusion detection method based on machine learning algorithm was proposed to detect attacks on MANET.The method captured the normal traffic's inter-feature correlation pattern which could be used as normal profiles to detect anomalies caused by attacks.The method was implemented on Ad-hoc On-Demand Distance Vector(AODV) protocol and evaluated in QualNet,a leading network simulation software.