On the feasibility of an embedded machine learning processor for intrusion detection
Rajesh Sankaran, Ricado A. Calix · 2016
Intrusion detection and prevention systems serve a pivotal role in securing computer networks. Using machine learning for an intrusion detection system is important for stopping new attacks that do not have known signatures. Lowering the barrier to entry for microprocessor-based systems has enabled the use of specialized machine learning coprocessors to improve analysis performance. This paper proposes a machine learning approach on a small, low-powered embedded system that uses network-based features to distinguish between normal and abnormal network traffic. A hardware-based approach using a machine learning coprocessor is compared with a software-based approach. Machine learning processors can improve power consumption and processing speed especially when dealing with dig data sets. Results of the analysis show that the machine learning coprocessor obtains 66.67% classification accuracy. Additional results are presented and discussed.