A Review of Machine Learning Applications in Fuzzing

Autor:	Saavedra, Gary J, Rodhouse, Kathryn N, Dunlavy, Daniel M, Kegelmeyer, Philip W
Rok vydání:	2019
Předmět:	Computer Science - Cryptography and Security Computer Science - Artificial Intelligence Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1906.11133 Zobrazit plný text záznamu View this record from Arxiv