Towards the Development of a Realistic Multidimensional IoT Profiling Dataset
Bibliographic record
Abstract
The Internet of Things (IoT) is an emerging technology that enables the development of low-cost and energy-efficient IoT devices across various solutions from smart cities to healthcare domains. With such a complex and heterogeneous instance of IoT devices and their applications, numerous challenges arise in both device management and security concerns. Thus, it is essential to develop intelligent IoT identification/profiling and intrusion detection components that are tailored to IoT applications. Such systems require a realistic and multidimensional reference IoT dataset for training and evaluation. Device identification/profiling ensures the authenticity of the devices attached to the IoT network and environment which can be achieved by fingerprinting a device. Since fingerprinting is mostly examined by device network flows and device local attributes, we have proposed this study to intelligently recognize machine-to-machine communication and identify each device properly. In this paper, we analyzed the behaviour of 60 IoT devices during experiments conducted in our lab setup at the Canadian Institute for Cybersecurity (CIC). Our IoT devices include WiFi, ZigBee, and Z-Wave devices. We collected data from each device in four stages: powered on, idle, active, and interactions. Besides these stages, different scenario experiments were conducted using a microcosm of devices to simulate the network activity of a smart home. Additionally, we have generated two attack datasets, namely flood denial-of-service attack and RTSP brute-force attack. Lastly, we implement an extensive case study on the transferability of the RF classifier and train our model with the dataset from our lab, transfer the model to the dataset from a different lab and test the trained model on their dataset. This paper’s dataset materials are available on the CIC dataset page under the CIC IoT dataset 20221.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".