In this paper, we present a new approach that can efficiently classify crashes based on their exploitability, which facilitates the human analysts to prioritize ...
Abstract—The proliferation of Internet of Things (IoT) devices is accompanied by the tremendous increase of the attack surface.
This paper presents a new approach that can efficiently classify crashes based on their exploitability, which facilitates the human analysts to prioritize ...
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Assisting Vulnerability Detection by Prioritizing Crashes with Incremental Learning (2018); Analyzing Evolving Trends of Vulnerabilities in National ...
... Tripathi et al. proposed training a binary classifier to determine whether or not a given crash due to bugs is exploitable. Their tool, called Exniffer, ...
A systematic review of fuzzing based on machine learning techniques
www.ncbi.nlm.nih.gov › PMC7433880
Assisting Vulnerability Detection by Prioritizing Crashes with Incremental Learning. In: TENCON 2018–2018 IEEE Region 10 Conference. IEEE; 2018.p. 2080–2085 ...
... Tripathi et al. proposed training a binary classifier to determine whether or not a given crash due to bugs is exploitable. Their tool, called Exniffer, ...
Vulnerability detection with deep learning. In: 2017 3rd IEEE ... Assisting Vulnerability Detection by Prioritizing Crashes with Incremental Learning.
Dec 3, 2022 · Assisting vulnerability detection by prioritizing crashes with incremental learning. In Proceedings of the TENCON IEEE Region 10 Conference ...
