CAVA: Cognitive Aid for Vulnerability Analysis

Simple item page
dc.contributor.authorKim, Evelyn
dc.contributor.authorFugate, Sunny
dc.contributor.authorLebiere, Christian
dc.contributor.authorBarbieux, Aidan
dc.contributor.authorBuch, Jonathan
dc.contributor.authorCho, Jaehoon
dc.contributor.authorCranford, Edward
dc.contributor.authorDivita, Joseph
dc.contributor.authorJohnson, Jeremy
dc.contributor.authorLevy, Mia
dc.contributor.authorMaldonado, Froylan
dc.contributor.authorMarsh, Brianna
dc.contributor.authorMorrison, Donald
dc.contributor.authorRego, Jocelyn
dc.contributor.authorSayer, Mitchell
dc.contributor.authorWaagen, Alex
dc.contributor.authorBhattacharyya, Rajan
dc.date.accessioned2023-12-26T18:53:46Z
dc.date.available2023-12-26T18:53:46Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.886
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other5589cadd-8ea2-4d1d-881d-4067472fa613
dc.identifier.urihttps://hdl.handle.net/10125/107272
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCyber Operations, Defense, and Forensics
dc.subjectclosed-loop visual aid
dc.subjectcognitive model
dc.subjectsoftware reverse engineering
dc.titleCAVA: Cognitive Aid for Vulnerability Analysis
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractBecoming a reverse engineer (RE) requires rigorous training and understanding of program structure and functionality, and experts develop heuristic strategies and intuitions from real-world experiences. This paper attempts to capture REs’ strategies and intuitions within a predictive cognitive model and demonstrate the feasibility of assisting novice REs using an intelligent recommender called CAVA (Cognitive Aid for Vulnerability Analysis). CAVA leverages physiological sensors to assess a novice’s cognitive states and provides real-time visual hints when the novice’s attention and engagement diminish. We instrumented Ghidra and conducted pilot experiments with REs. Open-loop experiments with 9 REs confirmed the feasibility of identifying novices from experts using physiological signals, and a pilot closed-loop experiment tested the feasibility of providing visual recommendations to a novice. Despite challenges in recruiting REs, our progress suggests that CAVA is a promising approach to improve novice performance and our understanding of experts’ behavior when performing complex real-world reverse engineering tasks.
dcterms.extent10 pages
prism.startingpage7377

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0720.pdf
Size:
2.45 MB
Format:
Adobe Portable Document Format
Download

Collections

Cyber Operations, Defense, and Forensics