  Machine learning is increasingly used for intrusion detection in IoT
networks. This paper explores the effectiveness of using individual packet
features (IPF), which are attributes extracted from a single network packet,
such as timing, size, and source-destination information. Through literature
review and experiments, we identify the limitations of IPF, showing they can
produce misleadingly high detection rates. Our findings emphasize the need for
approaches that consider packet interactions for robust intrusion detection.
Additionally, we demonstrate that models based on IPF often fail to generalize
across datasets, compromising their reliability in diverse IoT environments.
