Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks
The Internet of Things (IoT) is rapidly growing and expanding into various sectors, such as healthcare, transportation, smart homes, and more. Despite the benefits of using IoT devices, they present several challenges. Given the significant role these devices play in our lives, it is crucial to address issues related to their security and privacy. These devices are limited in resources, which complicates their security and the protection of the data that they manage. The paper aims to examine intrusion detection systems using the Gotham2025 dataset, generated through the Gotham testbed, which consists of 78 emulated IoT devices utilising various protocols, including MQTT, CoAP, and RTSP, ...
| Authors | Rana Alharbi, Chuadhry Mujeeb Ahmed |
|---|---|
| Primary category | cs.CR |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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