Early Detection of Cybersecurity Threats Using Collaborative Cognition

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dc.contributor.authorNarayanan, Sandeep Nair
dc.contributor.authorGanesan, Ashwinkumar
dc.contributor.authorJoshi, Karuna
dc.contributor.authorOates, Tim
dc.contributor.authorJoshi, Anupam
dc.contributor.authorFinin, Tim
dc.date.accessioned2020-07-23T17:00:27Z
dc.date.available2020-07-23T17:00:27Z
dc.date.issued2018-11-19
dc.description2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC), 18-20 Oct. 2018, Philadelphia, PA, USAen_US
dc.description.abstractThe early detection of cybersecurity events such as attacks is challenging given the constantly evolving threat landscape. Even with advanced monitoring, sophisticated attackers can spend more than 100 days in a system before being detected. This paper describes a novel, collaborative framework that assists a security analyst by exploiting the power of semantically rich knowledge representation and reasoning integrated with different machine learning techniques. Our Cognitive Cybersecurity System ingests information from various textual sources and stores them in a common knowledge graph using terms from an extended version of the Unified Cybersecurity Ontology. The system then reasons over the knowledge graph that combines a variety of collaborative agents representing host and network-based sensors to derive improved actionable intelligence for security administrators, decreasing their cognitive load and increasing their confidence in the result. We describe a proof of concept framework for our approach and demonstrate its capabilities by testing it against a custom-built ransomware similar to WannaCry.en_US
dc.description.sponsorshipThis research was conducted in the UMBC Accelerated Cognitive Computing Lab (ACCL), which is supported in part by a gift from IBM. We thank the other members of the ACCL Lab for their input in developing this system.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/8537852en_US
dc.format.extent10 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/m2gl8p-ccyt
dc.identifier.citationS. N. Narayanan, A. Ganesan, K. Joshi, T. Oates, A. Joshi and T. Finin, "Early Detection of Cybersecurity Threats Using Collaborative Cognition," 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC), Philadelphia, PA, 2018, pp. 354-363, doi: 10.1109/CIC.2018.00054.en_US
dc.identifier.uri10.1109/CIC.2018.00054
dc.identifier.urihttp://hdl.handle.net/11603/19228
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Information Systems Department
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rights© 2018 IEEE
dc.subjectUMBC Ebiquity Research Group
dc.titleEarly Detection of Cybersecurity Threats Using Collaborative Cognitionen_US
dc.typeTexten_US

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