IoT Network Intrusion Detection using Contrastive Learning with a Lightweight Autoencoder

Xinchen Zhang1; Edith C.-H. Ngai1; Shuang-Hua Yang2

10.1109/SWC57546.2023.10449070

2023-08-31

979-8-3503-1981-1

2023 IEEE Smart World Congress (SWC)

28-31 Aug. 2023

Portsmouth, United Kingdom

The IoT has experienced rapid growth in the past decade and is now facing an alarming increase in targeted cyber attacks 1 . As these attacks become more frequent and complex, it is crucial to create robust security strategies and intrusion detection methods to shield IoT networks. A key component of network security is Intrusion Detection System (IDS), working together with other defensive tools such as firewalls, antivirus applications, and encryption methodologies. The intrusion detection system (IDS) can enhance network security and complement other protective measures like firewalls, antivirus software, and encryption techniques.

其他

1.The University of Hong Kong
2.Southern University of Science and Technology

Xinchen Zhang,Edith C.-H. Ngai,Shuang-Hua Yang. IoT Network Intrusion Detection using Contrastive Learning with a Lightweight Autoencoder[C],2023.