A real-time automated attack-defense graph generation approach
Bibliographic record
Abstract
With the increase in cyberattacks, developing appropriate strategies to mitigate and prevent them is essential. In the literature, tools exist that either help prevent or mitigate them. Attack graphs help define mitigation strategies because they help represent and visualize the attacker’s position on a system. However, the mitigation actions are not instantiated on the attack graph. This paper proposes an approach to generate an automated attack-defense graph based on real-time monitored system alerts and an extensive and comprehensive state-of-the-art review. We propose to enrich logical attack graphs generated by a logical reasoner. The enrichment process is possible thanks to a vulnerability ontology that infers additional impacts for an exploited vulnerability. We propose a countermeasure selection approach based on graph matching to generate an optimal Incident Response (IR) playbook. We propose instantiating the generated playbook’s IR actions to get an attack-defense graph in real-time. This instantiation is done thanks to anti-correlation. The anti-correlation ensures that the countermeasures are instantiated on the appropriate attack graph nodes. Only the IR actions whose execution can be launched automatically are applied. We validate our approach using two use-case scenarios that target critical industrial infrastructures. We analyze the countermeasures instantiated on the attack graphs for the scenarios that can achieve the attack goal. We evaluated the approach concerning the security relevance of instantiated countermeasures in attack graphs for several attack paths. The countermeasures instantiated on a node are always relevant to the attacker’s action represented by this node. We also evaluate the approach regarding time performance, considering several situations for the use-case scenarios. The generation time depends on the number of vulnerabilities involved in the scenario. The generation time is on average 0.161 s when the playbook has been generated before the attack defense graph generation process.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".