CyberX 2.0: From Hacks to Head Games - Evolving Cyber Defence with Strategic Twists and Tactical Consequences
Bibliographic record
Abstract
CyberX is a unique, large-scale cyber operations exercise that incorporates a cyber-kinetic battlespace, designed to provide participants with a realistic, multifaceted problem space. The original environment offered limited support for Information Environment operations beyond scenarios for Defensive Cyber Operations, Offensive Cyber Operations, and Computer Network Exploitation. These scenarios did not initially include aspects of information operations or cognitive influence, such as diplomacy, propaganda, fake news, social media manipulation, and political subversion—key elements associated with hybrid warfare. This paper presents the ongoing evolution of CyberX, which introduces new dimensions of Information Operations to enhance the exercise scenarios and broaden learning opportunities for participants. The goal is to incorporate open-source intelligence and cognitive influence elements into Information Environment operations. New features include a geopolitical context for the mission scenario and a cognitive dimension to the Information Environment, ensuring that decisions made at the tactical cyberspace level carry real consequences. An integrated social media environment now supports Information Operations scenarios, populated by simulated personas and social media interactions. Exercise control referees use this platform to set up the scenario and manage gameplay. The platform leverages AI to semi-automatically generate message content, blending AI-generated rumors with ground-truth information. This simulated information space provides commanders with a more nuanced understanding of adversary disposition and movements. However, with this enhanced insight comes a greater strategic responsibility, requiring commanders to operate within the cognitive geopolitical space. This evolution makes the CyberX mission scenarios more tangible and realistic. The goal is to ensure that decisions made at the tactical cyberspace layer have real consequences. Choices aimed at locally optimizing risk in response to a cyber threat at the expense of overall mission success are discouraged. The learning outcomes now emphasize the integrated nature of cyber operations with other operational domains and their interdependence for mission success.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".