{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T08:48:03Z","timestamp":1782809283762,"version":"3.54.5"},"reference-count":0,"publisher":"ECMS","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,23]]},"abstract":"<jats:p>Security Operations Centers are currently inundated with high-volume, heterogeneous data streams that are extremely challenging to analyze cohesively to make timely cyber defense decisions. In this paper, we present the Attack Summarization Model (ASM), a novel multi-expert ensemble neural network that combines the power of graph neural networks for network data flows and long short-term memory networks for host-based events, enabling each expert model to make accurate predictions based on a single data source that can be combined into a holistic prediction of adversary behaviors. We describe the collection and engineering of ground truth datasets using networking benchmarks and live hacking competitions, and discuss a set of simulation experiments to train and validate the performance of the individual components of the ASM.<\/jats:p>","DOI":"10.7148\/2026-0331","type":"proceedings-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T08:36:46Z","timestamp":1782808606000},"page":"331-337","source":"Crossref","is-referenced-by-count":0,"title":["Multi-expert ensemble models for cyber adversary behavior prediction"],"prefix":"10.7148","author":[{"given":"Amy","family":"Sliva","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Murray","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bailey","family":"Gallagher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Logan","family":"Palys","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stoney","family":"Trent","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4144","published-online":{"date-parts":[[2026,6,23]]},"event":{"name":"40th ECMS International Conference on Modelling and Simulation"},"container-title":["ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina"],"original-title":[],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T08:36:56Z","timestamp":1782808616000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.scs-europe.net\/dlib\/2026\/ecms2026acceptedpapers\/0331_secmos_ecms2026_0055.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7148\/2026-0331","relation":{},"subject":[],"published":{"date-parts":[[2026,6,23]]}}}