Benchmarking Deep Learning Algorithms for Intrusion Detection IoT Networks
Hoummady Enzo, Fehmi Jaafar · 2024
With the increasing popularity of Internet of Things (IoT) and its connected devices, security has become a major concern. In this paper, we conducted a benchmark to evaluate performance of different deep learning algorithm device based on its network traffic. We developed our own dataset for our pilot study that included three different types of cyberattacks: reverse shell, keylogger, and synflood.The diversity and scope of our research has been enhanced by the incorporation of the CIC IoT dataset, which has been added to our initial work. We conducted a systematic evaluation of the performance of various deep learning models, which included CNNs and LSTM networks. Our benchmarking efforts on the CIC IoT dataset resulted in a significant improvement, with all models achieving an accuracy of over 99% and more than 93% on our custom dataset.