Intrusion Detection via Multilayer Perceptron using a Low Power Device

This work investigates the use of Multi-layered Perceptron Networks (MLP) for attack detection, using the Arduino embedded system as a case study. This paper also investigates techniques to reduce the computational cost of ANN (Artificial Neural Networks), taking into account the low cost and low consumption requirements in order to ensure the feasibility of its implementation. As a result, we evaluated the MLP networks using metrics such as accuracy, precision, and coverage, as well as the classifier performance running on Arduino through time measurements (microseconds).