An intrusion detection scheme based on anomaly mining in internet of things

Internet of things (IOT) is vulnerable to malicious attacks because of opening deployment and limited resources. It´s heterogeneous and distributed characters make conventional intrusion detection methodologies hard to deploy. To overcome this problem, this paper shows an intrusion detection scheme based on the anomaly mining. The paper has two parts (i) in the first part an anomaly mining algorithm is developed to detect anomaly data of perception layer, (ii) in the second part a distributed intrusion detection scheme is designed based on the detected anomalies. Since not all anomalies are triggered by malicious intrusion, the intrusion semantic is analyzed to distinguish intrusion behaviors from anomalies. Finally our evaluation and analysis shows that our approach is accurate and extensible.