Modelling Worm Outbreaks in Python to See Which Defences Actually Help

说明与用途

Python 模拟器可模拟恶意蠕虫在网络中的传播，比较不同拓扑和防御措施。早期补丁或隔离可显著降低感染数，而延迟响应几乎无效。

问题

How to evaluate the effectiveness of defensive measures against fast‑moving worm outbreaks when real incidents cannot be replayed.

结果

Early interventions (e.g., patch_all at tick 2) reduce infections from 396 to 7 in a 500‑node scale‑free graph, demonstrating that timing outweighs thoroughness.

工作流

• 1. Define network topology and worm parameters in a YAML scenario. 2. Load the scenario with create_simulator_from_config. 3. Run the simulation with sim.run(). 4. Retrieve metrics via sim.get_summary(). 5. Compare scenarios using the Typer CLI compare command or programmatically.
• 6. Visualise results with the Plotly module or FastAPI service.
• 7. Iterate by adjusting parameters (e.g., intervention_tick) to study impact.
• 8. Document findings and optionally contribute new scenarios to the repo.

证据

The Worm Propagation Simulator is a small Python project that models how a self‑replicating worm spreads across a network graph, and how defensive measures change the outcome.

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The model is simple, but this is the same lesson real incidents keep teaching. Against a fast scanner, a complete response that arrives late is worth almost nothing.

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The repo also includes: A Typer CLI, A FastAPI service, A Plotly visualisation module, Docker, Docker Compose and Kubernetes manifests.

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