Exploiting Ensemble Learning for Edge-assisted Anomaly Detection Scheme in e-healthcare System

With the thriving of wearable devices and the widespread use of smartphones, the e-healthcare system emerges to cope with the high demand of health services. However, this integrated smart health system is vulnerable to various attacks, including intrusion attacks. Traditional detection schemes generally lack the classifier diversity to identify attacks in complex scenarios that contain a small amount of training data. Moreover, the use of cloud-based attack detection may result in higher detection latency. In this paper, we propose an Edge-assisted Anomaly Detection (EAD) scheme to detect malicious attacks. Specifically, we first identify four types of attackers according to their attacking capabilities. To distinguish attacks from normal behaviors, we then propose a wrapper feature selection method. This selection method eliminates the impact of irrelevant and redundant features so that the detection accuracy can be improved. Moreover, we investigate the diversity of classifiers and exploit ensemble learning to improve the detection rate. To reduce high detection latency in the cloud, edge nodes are used to concurrently implement the proposed lightweight scheme. We evaluate the EAD performance based on two real-world datasets, i.e., NSL-KDD and UNSW-NB15 datasets. The simulation results show that the EAD outperforms other state-of-the-art methods in terms of accuracy, detection rate, and computational complexity. The analysis of detection time validates the fast detection of the proposed EAD compared with cloud-assisted schemes.