IoT Network Intrusion Detection using Contrastive Learning with a Lightweight Autoencoder

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.