Distributed ADP-Based Optimal Security Control of Multiagent Systems Against DoS Attacks Within Differential Adversarial Game Framework
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 article, a distributed optimal security control method is proposed for multiagent systems (MASs) containing multiple attackers and defenders within a differential adversarial game framework. Initially, the control inputs of the defenders’ systems are considered to suffer from denial-of-service (DoS) attacks from the attackers, then the coupled performance index functions associated with the state errors are constructed. By using the distributed adaptive dynamic programming (ADP) technology, a modified radial basis function neural network (NN) is implemented such that the coupled performance index functions are approximately identified, and by solving the coupled Hamilton-Jacobi-Bellman (HJB) equation, an ADP-based optimal security control policy with a single-critic NN updating law is further proposed. Meanwhile, it is proven that the proposed optimal security control policy constitutes the Nash equilibrium point of the differential adversarial game. Finally, a simulation example is given to validate the effectiveness of the proposed distributed optimal security control method.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it