Strategy-Aware Mitigation Using Markov Games for Dynamic Application-Layer Attacks
Bibliographic record
Abstract
Targeted and destructive nature of strategies used by attackers to break down the system require mitigation approaches with dynamic awareness. In the domain of adaptive software security, the adaptation manager of a self-protecting software is responsible for selecting countermeasures to prevent or mitigate attacks immediately. Making a right decision in each and every situation is one of the most challenging aspects of engineering self-protecting software systems. Inspired by the game theory, in this research work, we model the interactions between the attacker and the adaptation manager as a two-player zero-sum Markov game. Using this game-theoretic approach, the adaptation manager can refine its strategies in dynamic attack scenarios by utilizing what has learned from the system's and adversary's actions. We also present how this approach can be fitted to the well-known MAPE-K architecture model. As a proof of concept, this research conducts a study on a case of dynamic application-layer denial of service attacks. The simulation results demonstrate how our approach performs while encountering different attack strategies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".