Joint Security and Resources Allocation Scheme Design in Edge Intelligence Enabled CBTCs: A Two-Level Game Theoretic Approach
Bibliographic record
Abstract
The increasingly intense cyber-attacks have always been a crucial issue to the communication-based train control (CBTC) system due to exposed wireless channels. Both cyber-attack intrusion detection and defense policy calculation demand substantial computing resources. Combined with high capacity and reliability 5G technologies, edge intelligence (EI) is believed to help empower CBTC systems in terms of security and efficiency. This paper proposes an EI-enabled structure for CBTCs to defend against cyber-attacks, where the EI server provides real-time intelligent computing services for trains to derive real-time defense policies. We formulate the cyber-attack and defense process in EI-enabled CBTCs as a two-level game model, where system security and edge computing resource allocation are jointly optimized. In the lower-level game, we model interactions between the cyber attacker and system defender as a discrete repeated security game (DRSG), which is also a non-zero sum and incomplete information game. The fictitious play (FP) is introduced to derive a Nash equilibrium (NE) based optimal defense scheme. In the upper-level game, considering that the EI server cannot simultaneously update the optimal defense scheme for all trains due to the limited computation resources, we construct a multi-stage computation resource allocation game (MCRAG). We derive the optimal computation resource allocation scheme by the neural fictitious self-play (NFSP), where a deep Q-learning network (DQN) and a supervised learning network are jointly built to learn the strategy. Extensive simulation results show that our proposed EI-enabled CBTC system and the two-level game model can effectively defend against various attacks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".