eDoS Mitigation for Autonomic Management on Multi-Tier IoT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this age of the Internet of Things and ubiquitous computing, autonomic management has become a critical component in cloud platforms. Autonomic management helps systems adapt seamlessly and efficiently to rapidly fluctuating workloads. However, economic Denial of Sustainability (eDoS) attacks can directly target the autonomic management to waste resources. In this paper, we propose an eDoS mitigation framework that incorporates online anomaly detection with our Elascale autonomic management system to thwart eDoS attacks in real-time. This allows the detection system to be application-agnostic as this framework utilizes only resource statistics of the monitoring applications. We present the design and implementation of our anomaly detection framework with Elascale. We evaluate Hierarchical Temporal Memory (HTM) and Tukey with Relative Entropy against spatial and temporal anomalies. Our results prove that the HTM-based anomaly detection method outperforms with significant accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it