This challenge seeks AI systems that can learning network behaviour, anticipating attacker progression and supporting proactive cyber defence using the emerging concept of World Models. Design and develop a software prototype that learns the evolving state of a computer network from traffic telemetry and predicts the likelihood and progression of malicious activity before compromise is completed. The expected system must ingest network traffic, learn temporal behaviour, forecast future attack state
Participants are encouraged to build world models based AI systems that move beyond static intrusion classification towards predictive cyber defence.
This challenge seeks AI systems that can learning network behaviour, anticipating attacker progression and supporting proactive cyber defence using the emerging concept of World Models Design and develop a software prototype that learns the evolving state of a computer network from traffic telemetry and predicts the likelihood and progression of malicious activity before...
Participants are encouraged to build world models based AI systems that move beyond static intrusion classification towards predictive cyber defence. The solution may utilise flow records, packet captures, authentication logs or other publicly available cybersecurity telemetry. It should model temporal relationships, infer evolving network state, predict future attack progression and present meaningful explanations for its predictions. Traditional machine learning classifiers applied to network traffic treat each flow in isolation and map it to a binary benign/malicious label. This discards the temporal and causal structure of an infiltration: the sequence in which ports are probed, the pattern in which SYN flags precede ACK floods, the inter-arrival timing of reconnaissance packets before