Towards an Energy-Efficient Anomaly-Based Intrusion Detection Engine for Embedded Systems

Nowadays, a significant part of all network accesses comes from embedded and battery-powered devices, which must be energy efficient. This paper demonstrates that a hardware (HW) implementation of network security algorithms can significantly reduce their energy consumption compared to an equivalent software (SW) version. The paper has four main contributions: (i) a new feature extraction algorithm, with low processing demands and suitable for hardware implementation; (ii) a feature selection method with two objectives-accuracy and energy consumption; (iii) detailed energy measurements of the feature extraction engine and three machine learning (ML) classifiers implemented in SW and HW-Decision Tree (DT), Naive-Bayes (NB), and k-Nearest Neighbors (kNN); and (iv) a detailed analysis of the tradeoffs in implementing the feature extractor and ML classifiers in SW and HW. The new feature extractor demands significantly less computational power, memory, and energy. Its SW implementation consumes only 22 percent of the energy used by a commercial product and its HW implementation only 12 percent. The dual-objective feature selection enabled an energy saving of up to 93 percent. Comparing the most energy-efficient SW implementation (new extractor and DT classifier) with an equivalent HW implementation, the HW version consumes only 5.7 percent of the energy used by the SW version.