Toward generating a large-scale IoT-Zwave intrusion detection dataset: Smart device profiling, intruders behavior, and traffic characterization
Bibliographic record
Abstract
The rapid expansion of the Internet of Things (IoT) has introduced critical security challenges, making IoT ecosystems a prime target for cyber threats. Traditional security measures, relying on predefined signatures and static rules, struggle to detect sophisticated attacks that evolve dynamically. While machine learning and deep learning have improved IoT security, their effectiveness is fundamentally limited by the quality and diversity of available datasets. Existing IoT security datasets suffer from numerous shortcomings, including limited device diversity, inadequate threat coverage, the absence of real-world user and environment interaction, a lack of IoT-specific attacks, insufficient data volume, outdated threat scenarios, a lack of multimodal data, and a lack of support for multi-protocol analysis. To bridge this gap, we conducted a comprehensive analysis of the top 30 publicly available IoT smart home datasets, identifying 22 critical shortcomings that hinder their applicability in security research. To address these limitations, we introduce BCCC-IoT-IDS-Zwave-2025, the most extensive and diverse IoT smart home dataset to date, developed over five months using a large-scale testbed comprising more than 50 IoT devices and encompassing over 80 distinct attack scenarios. Unlike prior datasets that focus primarily on IP network-layer traffic, our dataset integrates multi-source data, including IP-based network traffic, IoT-Zwave communication signals, device activity, and MQTT-based traffic and logs, with attack scenarios specifically designed for each data source, enabling a holistic view of IoT threats. To further enhance IoT threat analysis, we developed IoT-ZwaveNetLyzer, the first dedicated traffic analyzer for Z-Wave networks, addressing the gap left by traditional PC-focused tools. Extensive experimental evaluations demonstrate the dataset’s effectiveness, with state-of-the-art classifiers achieving an average detection accuracy exceeding 95% and a false positive rate as low as 2.2% on average, establishing BCCC-IoT-IDS-Zwave-2025 as a cornerstone for future IoT security research and the development of advanced detection methodologies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".