CMU SEI Research Review 2020 Day 2 Artifacts
• Collection
Publisher
Software Engineering Institute
Subjects
Abstract
This collection includes materials from day two of the 2020 SEI Research Review event. These materials include presentations and posters about automated code repair, vulnerability analysis, cyber opera-tor tradecraft, artificial intelligence defense evaluation, static analysis classification, and human-machine teaming.
Collection Items
Advancing Cyber Operator Tradecraft Through Automated Static Binary Analysis
• Presentation
By Edward J. Schwartz, Cory Cohen
This presentation discusses three SEI research and development projects that help malware and vulnerability analysts.
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Poster - Improvements to Object Oriented Construct Recovery Using OOAnalyzer
• Poster
By Cory Cohen, Edward J. Schwartz
This poster describes OOAnalyzer, which is now 50x faster and can analyze large programs.
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Poster - Program Reachability for Vulnerability and Malware Analysis
• Poster
By Edward J. Schwartz
This project automates analysis of binary code, choosing inputs that trigger specific behavior and reduce the time spent performing complex software analysis.
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Poster - Recovering Meaningful Variable Names in Decompiled Code
• Poster
By Edward J. Schwartz, Cory Cohen
This presentation describes DIRE, a novel probabilistic technique for variable name recovery that uses lexical and structural information.
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Automated Code Repair to Ensure Memory Safety for Source and Binary
• Presentation
By William Klieber
This presentation describes an automated technique developed to repair C source code to eliminate memory safety vulnerabilities.
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Poster - Automated Code Repair to Ensure Memory Safety (2020)
• Poster
By William Klieber
This poster describes an automated technique to repair C source code to eliminate memory safety vulnerabilities.
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About the President's Cup Cybersecurity Competition
• Poster
By Matt Kaar
The competition highlights cybersecurity talent in the federal government and promotes careers in the field.
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Artificial Intelligence Defense Evaluation
• Presentation
By Shing-hon Lau, Grant Deffenbaugh
This presentation describes efforts to develop a comprehensive testing methodology for AI defenses to identify their capabilities and the ways they can be bypassed.
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Static Analysis Classification: Line-Funded Research FY16-20
• Presentation
By Lori Flynn
CMU SEI researchers developed several static analysis techniques and tools to enable practical classification that leads to more secure software and lowered cost.
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Toward the Use of Human-Machine Teaming and Fully Autonomous Operations
• Presentation
By Jeff Mattson
This presentation describes CMU SEI's work in cyber workforce development and autonomous cyber operations.
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CMU SEI Research Review 2020 Artifacts