When Does Representation Learning Help Attack-Path Forecasting? A Controlled Multi-Cloud Study with CAPTURE-MC

Cloud defenders increasingly want to anticipate an attacker’s next move, not merely detect the last one. We study attack-path forecasting in multi-cloud environments: given the partial trace of an in-progress campaign, predict the next ATT&CK technique. We present CAPTURE-MC, a heterogeneous temporal-graph model that encodes cross-provider telemetry and forecasts the next technique under a transition prior, and we release MC-PathBench, a synthetic but structurally faithful benchmark with four leakage-controlled generalization splits (temporal, cross-cloud, unseen-composition, and campaign hold-out). Our central contribution is an honest, carefully controlled account of when learned temporal-graph representations help and when they do not. We find that the technique-transition structure is a very strong predictor: when the attacker’s current technique is known, a first-order Markov model and a uniform-over-allowed-transitions rule match or exceed CAPTURE-MC and a temporal-graph network, so the deep models add little in that idealized regime. The picture reverses in the realistic regime where the current technique must be inferred: as current-technique certainty degrades, the structural baselines, indexed entirely by it, collapse toward chance, whereas CAPTURE-MC degrades gracefully and nearly doubles their accuracy when the current technique is unknown. CAPTURE-MC is likewise robust to a substantially corrupted transition prior (missing, spurious, or misdirected edges). We also report, transparently, where CAPTURE-MC does not help: gradient-boosted trees remain the best per-event and campaign-level detectors; next-entity and time-to-transition prediction are at chance for every model; and the conformal layer, while achieving coverage, yields prediction sets larger than the list of allowed successors and so is not an efficiency gain. The prior is estimated only from training data, never the generator’s graph, which removes a leakage path present in an earlier version of this work and changes several conclusions. The practical lesson for predictive cloud defense is that structural baselines are strong and must be reported, and that the value of representation learning appears precisely where structural assumptions break down.