TITLE: Externally validating the IoTDevID device identification methodology using the CIC IoT 2022 Dataset 

ABSTRACT: In the era of rapid IoT device proliferation, recognizing, diagnosing, and
securing these devices are crucial tasks. The IoTDevID method (IEEE Internet of
Things 2022) proposes a machine learning approach for device identification
using network packet features. In this article we present a validation study of
the IoTDevID method by testing core components, namely its feature set and its
aggregation algorithm, on a new dataset. The new dataset (CIC-IoT-2022) offers
several advantages over earlier datasets, including a larger number of devices,
multiple instances of the same device, both IP and non-IP device data, normal
(benign) usage data, and diverse usage profiles, such as active and idle
states. Using this independent dataset, we explore the validity of IoTDevID's
core components, and also examine the impacts of the new data on model
performance. Our results indicate that data diversity is important to model
performance. For example, models trained with active usage data outperformed
those trained with idle usage data, and multiple usage data similarly improved
performance. Results for IoTDevID were strong with a 92.50 F1 score for 31
IP-only device classes, similar to our results on previous datasets. In all
cases, the IoTDevID aggregation algorithm improved model performance. For
non-IP devices we obtained a 78.80 F1 score for 40 device classes, though with
much less data, confirming that data quantity is also important to model
performance.
In the era of rapid IoT device proliferation, recognizing, diagnosing, and
securing these devices are crucial tasks. The IoTDevID method (IEEE Internet of
Things 2022) proposes a machine learning approach for device identification
using network packet features. In this article we present a validation study of
the IoTDevID method by testing core components, namely its feature set and its
aggregation algorithm, on a new dataset. The new dataset (CIC-IoT-2022) offers
several advantages over earlier datasets, including a larger number of devices,
multiple instances of the same device, both IP and non-IP device data, normal
(benign) usage data, and diverse usage profiles, such as active and idle
states. Using this independent dataset, we explore the validity of IoTDevID's
core components, and also examine the impacts of the new data on model
performance. Our results indicate that data diversity is important to model
performance. For example, models trained with active usage data outperformed
those trained with idle usage data, and multiple usage data similarly improved
performance. Results for IoTDevID were strong with a 92.50 F1 score for 31
IP-only device classes, similar to our results on previous datasets. In all
cases, the IoTDevID aggregation algorithm improved model performance. For
non-IP devices we obtained a 78.80 F1 score for 40 device classes, though with
much less data, confirming that data quantity is also important to model
performance.
Externally validating the IoTDevID device identification methodology using the CIC IoT 2022 Dataset
http://arxiv.org/abs/2307.08679v1