Better pay attention whilst fuzzing

• Main Authors: ZHU, Shunkai, WANG, Jingyi, SUN, Jun, YANG, Jie, LIN, Xingwei, ZHANG, Liyi, CHENG, Peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Attention Model; Codes; Computer bugs; Deep learning; Electronic mail; Fuzzing; Image edge detection; Program Analysis; Recurrent neural networks; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8550; https://ink.library.smu.edu.sg/context/sis_research/article/9553/viewcontent/Fuzzing_av.pdf
• Institution: Singapore Management University

Fuzzing is one of the prevailing methods for vulnerability detection. However, even state-of-the-art fuzzing methods become ineffective after some period of time, i.e., the coverage hardly improves as existing methods are ineffective to focus the attention of fuzzing on covering the hard-to-trigger...