iPASecIoT: An intelligent pipeline for automatic and adaptive feature extraction for secure IoT device identification and intrusion detection
Bibliographic record
Abstract
The widespread integration of Internet of Things (IoT) devices has enhanced the intelligence of homes, industries, and offices, yet it introduces critical security challenges due to their susceptibility to dynamic threats and behavioral heterogeneity, necessitating identification via communication patterns rather than mere physical recognition. This paper addresses the demand for a unified security framework in IoT ecosystems, where devices, limited by diverse protocols and constrained computational resources, face attacks such as DNS tunneling, MAC spoofing, and several other threats. Existing approaches, which rely on coarse-grained signatures or segregated machine learning for device identification and intrusion detection, exhibit limited resilience, increased operational overhead, poor cross-network adaptability, and scalability constraints in real-time dynamic settings. We propose iPASecIoT, a single-model framework that concurrently identifies IoT devices and detects intrusions using fine-grained behavioral fingerprints. Our methodology combines machine and deep learning algorithms with a modified firefly algorithm employing a kappa score-based voting mechanism for adaptive feature selection, yielding a lightweight, resource-efficient model by optimizing agreement beyond chance across network traffic, inter-arrival times, and protocol-specific features. Evaluated on the CICIoMT2024, CICIoT2023, and UNSW2019 datasets, iPASecIoT achieves mean F1 scores of 99.99 %, 99.88 %, and 98.35 % for device identification and 99.96 %, 99.38 %, and 98.79 % for threat classification across the CICIoMT2024, CICIoT2023, and UNSW2019 datasets, respectively. With a mean inference time of 0.0005 seconds per sample and a mean Hamming loss of ≈ 0.001, iPASecIoT provides a pioneering, efficient, and scalable solution to counter evolving security threats in heterogeneous IoT environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".