HoloTiny-AD: A Trustworthy Anomaly Detection in Resource-Constrained IoT Devices Using Holographic TinyML and Deep Metaheuristics
Bibliographic record
Abstract
Mobile Edge Computing (MEC) security and performance may be threatened by network traffic anomalies such as illegal data transfers or access. Traditional detection methods, such as machine learning, statistical models, and signature-based methods, are ineffective for real-time monitoring in resource-constrained devices. TinyML deploys lightweight models on resource-limited devices for rapid, localized, low-power threat detection without cloud access, improving MEC real-time security. This paper introduces HoloTiny-AD, a real-time anomaly detection technique in MEC using the Holographic Counterpart (HC) architecture, TinyML, and Deep Particle Swarm Optimization (DPSO). The proposed approach effectively monitors and detects anomalies while using minimum computing resources, making it suitable for MEC. HC uses TinyML models trained with lightweight classifiers to mimic device state transitions and analyze anomalies to identify potential risks. DPSO optimizes parameters and task scheduling to increase detection accuracy and predict overload or busy state device failures. The proposed methodology is evaluated on two benchmark datasets: CIC-BCCC-NRC-ACI-IOT-2023 and CIC-IDS2017. Results show that HoloTiny-AD is a robust, scalable solution for securing edge devices against evolving network threats under resource constraints.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".