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dc.contributor.advisorVanamala, Mounika
dc.contributor.authorLasky, Nicholas
dc.contributor.authorHallis, Benjamin
dc.date.accessioned2023-11-13T18:02:56Z
dc.date.available2023-11-13T18:02:56Z
dc.date.issued2022-04
dc.identifier.urihttp://digital.library.wisc.edu/1793/84711
dc.descriptionColor poster with text, charts, and graphs.en_US
dc.description.abstractAs cyber-attacks increase in volume and sophistication, the current state of cybersecurity solutions is inadequate. According to Red Canary, Advanced Persistent Threat (APT) attacks have increased from approximately 500 attacks per year in 2009 to almost 2,500 APT attacks per year in 2019. The lack of timely detection and response is mainly caused by the insufficient support of attack action correlations and prediction to allow for proactive intrusion, investigation, and mitigation. There are lots of different ways to try and get past security, and it is difficult to create a system that protects against every single one. With the vast amount of data available online, it is necessary to integrate security-related activities and deliverables into each phase of the software development life cycle.en_US
dc.description.sponsorshipUniversity of Wisconsin--Eau Claire Office of Research and Sponsored Programsen_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesUSGZE AS589;
dc.subjectMachine learningen_US
dc.subjectCybersecurityen_US
dc.subjectSystems thinkingen_US
dc.subjectPostersen_US
dc.subjectDepartment of Computer Scienceen_US
dc.titleMachine Learning Based Approach to Recommend MITRE ATT&CK Framework for Software Requirements and Design Specifications : AIMS (Artificial Intelligence, Machine Learning, and Security) Research Groupen_US
dc.typePresentationen_US


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    Posters of collaborative student/faculty research presented at CERCA

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