A Review of Machine Learning Applications in Fuzzing

June 13, 2019 · The Cartographer · 🏛 arXiv.org

📚 THE CARTOGRAPHER: The Cartographer
Survey/review paper — maps the landscape rather than implementing a method.

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"Title-pattern auto-detect: A Review of Machine Learning Applications in Fuzzing"

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Authors Gary J Saavedra, Kathryn N Rodhouse, Daniel M Dunlavy, Philip W Kegelmeyer arXiv ID 1906.11133 Category cs.CR: Cryptography & Security Cross-listed cs.AI, cs.LG, stat.ML Citations 31 Venue arXiv.org Last Checked 2 days ago
Abstract
Fuzzing has played an important role in improving software development and testing over the course of several decades. Recent research in fuzzing has focused on applications of machine learning (ML), offering useful tools to overcome challenges in the fuzzing process. This review surveys the current research in applying ML to fuzzing. Specifically, this review discusses successful applications of ML to fuzzing, briefly explores challenges encountered, and motivates future research to address fuzzing bottlenecks.
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