Aviation Network Security Situation Awareness Based on Game Theory
Bibliographic record
Abstract
As aviation industry has progressed in recent years, the entire industry has become more and more relying on digital network connectivity and electronic data exchange. While these changes bring efficiency, they also provide opportunities for hackers and malware to threaten flight safety. For example, in 2022, the Canadian airline Sunwing experienced flight delays due to cyber attacks. In many cases, if proper defense strategies are used early, the damage caused by the threat can be greatly reduced. However, due to dynamic, heterogeneous and huge structural characteristics of aviation systems, managers are unable to assess the current system security status in a real-time and accurate way, which affects the decision-making process and leads to expose the whole system under the risk. Therefore, aviation networks need a situational awareness mechanism to help managers adjust their strategies and limit the impact of threats in a timely and accurate manner. There has been a lot of current research in network security situational awareness, but there are some problems when these studies are directly applied to aviation networks. In order to establish a situation awareness mechanism applicable to aviation networks, this paper proposes a situation awareness method based on game theory, particle swarm optimization algorithm and neural network by analyzing the strategy gain of attackers and defenders under network attack and defense, particle swarm optimization algorithm and long short-term memory networks. The innovative works of this paper are as follows. Firstly, the situation assessment model is established based on the classification of attackers; secondly, the method of situation prediction with improved particle swarm optimization algorithm and long short-term memory networks is proposed. Simulation experiment shows that the method in this paper has the advantages of high evaluation efficiency and low error rate of prediction results, which can reason about the attacker class and help managers to make reasonable decisions.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".