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Collaborative Research: SaTC: CORE: Medium: Transforming Intrusion Detection Through Autom — NSF Award to University of Illinois a

The groundswell of available data and computation power to learn from data has produced advanced automation across many domains, but cybersecurity has lagged these trends. Cybersecurity data sharing comes primarily in the form of indicators of compromise (IoCs) that describe patterns or artifacts that have already been

Award titleCollaborative Research: SaTC: CORE: Medium: Transforming Intrusion Detection Through Autom
Award ID2425892
AwardeeUniversity of Illinois at Chicago
CityCHICAGO
StateIL
Amount obligated$350,000
Principal investigatorAnrin Chakraborti
ProgramSecure &Trustworthy Cyberspace, Unallocated Program Costs
Start date06/15/2025
AbstractThe groundswell of available data and computation power to learn from data has produced advanced automation across many domains, but cybersecurity has lagged these trends. Cybersecurity data sharing comes primarily in the form of indicators of compromise (IoCs) that describe patterns or artifacts that have already been classified as associated with malicious activity. Identifying malicious activity and distilling one or more IoCs from it, however, is often a manual process that is slowed and/or
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