Enhancing IoT Security via Optimized Dual Chain-Channel Device Identification and Attack Detection Scheme
Bibliographic record
Abstract
In a world of 8.2 billion people, there are more than 18 billion connected Internet of Things (IoT) devices in 2024. This massive adoption and application of IoT has increased security concerns due to their vulnerability to various cyberattacks. Identifying connected devices is a proactive measure to accurately provide high-level security. Therefore, this paper proposes a novel dual-purpose framework for simultaneous identification of IoT devices and attack detection. An improved Firefly Algorithm (FA) with Matthews-Correlation Coefficient (MCC)-based voting is applied for feature optimization. To achieve simultaneous device identification (DI) and attack detection (AD) functions, the ClassifierChain model integrates XGBoost (XGB), Random Forest (RF), and Decision Tree (DT) as metalearners. Additionally, a one-dimensional convolutional neural network (1DCNN) with a dual output channel is implemented. FA optimizes feature selection to improve computational efficiency and model performance, while ClassifierChain enables multioutput classification to identify device types and detect attackspecific traffic flows. Experimental results in three (3) benchmark IoT datasets demonstrate superior accuracy, precision, and recall compared to traditional approaches. By precisely identifying IoT devices and simultaneously detecting the type of traffic flow, the proposed method offers robustness and improvement to the existing literature that handles these tasks independently.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".