Distributed Variational Bayes-Based In-Network Security for the Internet of Things
Bibliographic record
Abstract
The past few years have witnessed the compelling applications of the Internet of Things (IoT) in our daily life. The explosive growth of the number of IoT devices also presents a great challenge in network security, especially the DDoS attack. Current DDoS defense mechanisms adopted out-of-band architecture, which is accomplished by a process that receives monitoring data from routers and switches, then analyzes that flow data to detect attacks. However, facing IoT devices growing rapidly, this out-of-band architecture confronted with limited processing capacity, bandwidth resources, and service assurance problems. Recently, with the development of the programming switch, it opens up new possibilities for in-network DDoS detection, where the detection algorithms could be directly implemented inside the routers and switches. Benefit from switch processing performance, the in-network mechanism could achieve high scalability and line speed performance. Therefore, in this article, we design a machine learning-based in-network DDoS detection framework. We implement the lightweight variational Bayes algorithm in each switch to detect the anomaly traffic. Besides, considering the shortage of training data in each switch, a centralized platform is introduced to synchronize parameters among distributed switches to realize collaborative learning. Extensive simulations are conducted to evaluate our proposed algorithm in comparison to some state-of-the-art schemes.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".