Towards feasibility of Deep-Learning based Intrusion Detection System for IoT Embedded Devices
Jonathan Hunter, Brennan Huber, Farah I. Kandah · 2022
In this work we seek to determine the feasibility of implementing deep learning-based intrusion detection on higher-capacity embedded devices, by evaluating the performance metrics of pre-trained models on devices of varying resource capacity. Four deep learning models, trained on separate well-known intrusion detection dataset, will be deployed on each device. With an initial evaluation of neural network architecture, activation functions, and accuracy and later comparisons will include precision, f1 score, recall, and prediction rate, or time to predict per sample. Additionally, separate datasets will be used to observe model responses to new attack patterns.