Dynamic Temporal Positional Encodings for Early Intrusion Detection in IoT

--The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges, necessitating efficient and adaptive Intrusion Detection Systems (IDS). Traditional IDS models often overlook the temporal characteristics of network traffic, limiting their effectiveness in early threat detection. We propose a Transformer-based Early Intrusion Detection System (EIDS) that incorporates dynamic temporal positional encodings to enhance detection accuracy while maintaining computational efficiency. Additionally, we introduce a data augmentation pipeline to improve model robustness. Evaluated on the CICIoT2023 dataset, our method outperforms existing models in both accuracy and earliness. We further demonstrate its real-time feasibility on resource-constrained IoT devices, achieving low-latency inference and minimal memory footprint. The Internet of Things (IoT) enables smart devices to exchange data in real time across various domains, such as smart homes, healthcare, and industrial automation. These systems integrate the physical and digital worlds, generating diverse data types while often operating autonomously.