Dynamic Temporal Positional Encodings for Early Intrusion Detection in IoT
This work addresses security challenges in IoT by enhancing early threat detection, though it appears incremental as it builds on existing Transformer methods with specific adaptations.
The paper tackles the problem of early intrusion detection in IoT by proposing a Transformer-based system with dynamic temporal positional encodings, achieving improved accuracy and earliness on the CICIoT2023 dataset while demonstrating real-time feasibility on resource-constrained devices.
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. By leveraging network flow timestamps, our approach captures both sequence structure and timing irregularities indicative of malicious behaviour. 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.