Environment-Adaptive Malicious Node Detection in MANETs with Ensemble Learning
Boqi Gao, Takuya Maekawa, Daichi Amagata, Takahiro Hara · 2018
This paper presents a robust machine learning-based method for detecting malicious nodes in mobile ad hoc networks (MANETs). Since general machine learning methods rely on training data, trained detectors do not work well in test environments that are different from training environments. This is an inherent problem in malicious detection in dynamic MANETs environments, where network parameters, such as the average node speed and density of nodes, differ in each environment. In this study, we propose a method for environment-adaptive malicious node detection based on ensemble learning. We first prepare weak malicious node detectors trained in diverse environments, and then construct a strong ensemble malicious node detector, which is tailored to a given test environment, by fusing weak detectors whose performances are estimated to be high in the test environment. We investigate the performance of our method and confirm that our method significantly outperforms the state-of-the-art methods in terms of detection accuracy and false detection rate.