Mitigating dynamic attacks using multi-agent game-theoretic techniques
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
Emerging technologies such as mobile and cloud computing have given rise to new security vulnerabilities and challenges. At the same time, attackers utilize these technologies to initiate sophisticated attacks and exploit known and unknown vulnerabilities. A unique characteristic of recent attacks is their dynamic nature which allows attackers to stay stealth from Intrusion Detection Systems (IDSs). The proactive and dynamic nature of these security attacks make their detection and consequently their mitigation challenging. This demands fast reacting adaptive systems that are capable of detecting and mitigating threats on the fly. Our novel approach aims at addressing this demand by engineering a Self-Protecting Software (SPS) that incorporates attacker's possible strategies when selecting countermeasures. To achieve this goal, we propose a technique to model objectives of the attacker and SPS by the aid of multi-agent concepts, and utilize game theory to model the competition between the adaptation manager in SPS and the attacker.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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