Detection of Known and Unknown Intrusive Sensor Behavior in Critical Applications
Safa Otoum, Burak Kantarcı, Hussein Talaat Mouftah · IEEE Sensors Letters · 2017
This article presents a hybrid architecture to identify intrusive behavior among networked sensors that monitor critical systems such as environment, medical, and smart grid. Monitoring through sensors is desired for critical applications such as epilepsy seizures, pollution, power quality assessment, and transformer monitoring. Wireless sensors are being widely used in critical applications due to their advantages including low-cost, flexibility, and communication efficiency. However, when sensors are networked to monitor a critical infrastructure such as the smart grid, they become the target of different types of attackers such as intruders via the communication medium. In order to maintain sensing in a secure manner, robust architectures are needed to identify intrusive behavior of sensors in a network. In this article, we present a hybrid architecture to detect intrusive behavior of sensors for both unknown and known intruders. The former requires anomaly detection, whereas the latter requires signature detection. The proposed architecture consists of two subsystems that co-operate to detect unknown and known attacks through duty-cycling of enhanced density-based spatial clustering of applications with noise and random forest methods. Through various tests on real intrusion data, we show that the proposed architecture has a strong potential to detect both known and unknown intrusive behavior of sensor nodes as the results show 99.73% detection rate with 98.95% overall accuracy.