Deep learning based intelligent IDS for Large-scale IoT networks

TL;DR

This paper introduces two lightweight deep learning-based intrusion detection systems, CNN and LSTM, designed to improve security in large-scale IoT networks by accurately identifying various cyber threats.

Contribution

It proposes novel CNN and LSTM-based IDS models specifically optimized for IoT security, evaluated on the CICIoT2023 dataset with high accuracy results.

Findings

CNN-based IDS achieves up to 99.34% accuracy.

LSTM-based IDS achieves up to 99.42% accuracy.

Both models effectively classify multiple types of cyber threats.

Abstract

The proliferation of large-scale IoT networks has been both a blessing and a curse. Not only has it revolutionized the way organizations operate by increasing the efficiency of automated procedures, but it has also simplified our daily lives. However, while IoT networks have improved convenience and connectivity, they have also increased security risk due to unauthorized devices gaining access to these networks and exploiting existing weaknesses with specific attack types. The research proposes two lightweight deep learning (DL)-based intelligent intrusion detection systems (IDS). to enhance the security of IoT networks: the proposed convolutional neural network (CNN)-based IDS and the proposed long short-term memory (LSTM)-based IDS. The research evaluated the performance of both intelligent IDSs based on DL using the CICIoT2023 dataset. DL-based intelligent IDSs successfully identify and classify various cyber threats using binary, grouped, and multi-class classification. The proposed CNN-based IDS achieves an accuracy of 99.34%, 99.02% and 98.6%, while the proposed LSTM-based IDS achieves an accuracy of 99.42%, 99.13%, and 98.68% for binary, grouped, and multi-class classification, respectively.