A reliable and energy-efficient classifier combination scheme for intrusion detection in embedded systems

Embedded systems (electronic systems with a dedicated purpose that are part of larger devices) are increasing their relevance with the rise of the Internet of Things (IoT). Such systems are often resource constrained, battery powered, connected to the internet, and exposed to an increasing number of threats. An approach to detect such threats is through an anomaly-based intrusion detection with machine-learning techniques. However, most of these techniques were not created with energy efficiency in mind. This paper presents an anomaly-based method for network intrusion detection in embedded systems. The proposed method maintains the classifier reliability even when network traffic contents changes. The reliability is achieved through a new rejection mechanism and a combination of classifiers. The proposed approach is energy-efficient and well suited for hardware implementation. The experiments presented in this paper show that the hardware versions of the machine learning algorithms consume 46% of the energy used by their software counterparts, and the feature extraction and packet capture modules consume 58% and 37% of their respective software counterparts.