Robust, effective and resource efficient deep neural network for intrusion detection in IoT networks.

Zakariyya, Idris; Kalutarage, Harsha; Al-Kadri, M. Omar

Abstract

Internet of Things (IoT) devices are becoming increasingly popular and an integral part of our everyday lives, making them a lucrative target for attackers. These devices require suitable security mechanisms that enable robust and effective detection of attacks. Deep Neural Networks (DNNs) offer a promise, but they require large amounts of computational resources to provide better detection, and their detection capabilities can be exploited by adversarial attacks. Therefore, this paper proposes a method to train Fully Connected Neural Network (FCNN) for IoT security monitoring in a robust, effective and resource-efficient way. The resulting model is assessed against various benchmark datasets created using commercial IoT devices, such as doorbells, security cameras, and thermostats. Experimental results demonstrate the model's ability to maintain state-of-the-art accuracy and F1-score while reducing training memory and time consumption by 99.99 and 99.80 percentage points than its benchmark counterpart.

Citation

ZAKARIYYA, I., KALUTARAGE, H. and AL-KADRI, M.O. 2022. Robust, effective and resource efficient deep neural network for intrusion detection in IoT networks. In CPPS '22: proceedings of the 8th ACM (Association for Computing Machinery) Cyber-physical system security workshop 2022 (CPSS '22), co-located with the 17th ACM (Association for Computing Machinery) Asia conference on computer and communications security 2022 (ASIACCS '22) Nagasaki, Japan (virtual event). New York: ACM [online], pages 41-51. Available from: https://doi.org/10.1145/3494107.3522772

• Conference Name: 8th ACM (Association for Computing Machinery) Cyber-physical system security workshop 2022 (CPSS '22), co-located with the 17th ACM (Association for Computing Machinery) Asia conference on computer and communications security 2022 (ASIACCS '22)
• Conference Location: Nagasaki, Japan
• Start Date: May 30, 2022
• End Date: Jun 2, 2022
• Acceptance Date: Feb 7, 2022
• Online Publication Date: May 30, 2022
• Publication Date: May 31, 2022
• Deposit Date: Jul 29, 2022
• Publicly Available Date: Jul 29, 2022
• Publisher: Association for Computing Machinery (ACM)
• Pages: 41-51
• ISBN: 9781450391764
• DOI: https://doi.org/10.1145/3494107.3522772
• Keywords: IoT; Intrusion detection; Deep neural network; Computational efficient IDS
• Public URL: https://rgu-repository.worktribe.com/output/1721565