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A Data-Driven Finite State Machine Model for Analyzing Security Vulnerabilities
Shuo Chen
, Zbigniew Kalbarczyk
, Jun Xu
,
Ravishankar K. Iyer
Coordinated Science Lab
Electrical and Computer Engineering
National Center for Supercomputing Applications (NCSA)
Carl R. Woese Institute for Genomic Biology
Information Trust Institute
Biomedical and Translational Sciences
Siebel School of Computing and Data Science
Center for Global Studies
Research output
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Contribution to conference
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Paper
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peer-review
Overview
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Dive into the research topics of 'A Data-Driven Finite State Machine Model for Analyzing Security Vulnerabilities'. Together they form a unique fingerprint.
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Keyphrases
Activity Level
25%
Analysis Operation
25%
Code Inspection
25%
Finite State Machine
75%
Finite State Machine Models
100%
Focused Source
25%
Format String Vulnerability
25%
Heap Overflow
50%
Input Validation Vulnerabilities
25%
Integer Overflow
25%
Modeling Approach
25%
Overflow Vulnerability
25%
Practical Usefulness
25%
Security Check
25%
Security Vulnerabilities
100%
Source Code
50%
Stack Overflow
25%
State Machine Model
25%
Vulnerability
100%
Vulnerability Database
25%
Vulnerability Report
25%
Computer Science
Buffer Overflow
16%
Code Inspection
16%
Data Analysis
16%
Finite-State Machine
100%
Input Validation
16%
Integer Overflow
16%
Reported Vulnerability
16%
Security Vulnerability
100%
State Machine Model
100%
Vulnerability Data
16%