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PG-VulNet: Detect Supply Chain Vulnerabilities in IoT Devic..:

, In: Proceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement,

Liu, Xin ; Wu, Yixiong ; Yu, Qingchen... - p. 205-215 , 2022

Link: https://dl.acm.org/doi/10.1145/3544902.3546240

RT 
T1 <cTitle>Proceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement</cTitle>: T1 <Title>PG-VulNet: Detect Supply Chain Vulnerabilities in IoT Devices using Pseudo-code and Graphs</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3546240&Exemplar=1&LAN=DE
				
A1 Liu, Xin
A1 Wu, Yixiong
A1 Yu, Qingchen
A1 Song, Shangru
A1 Liu, Yue
A1 Zhou, Qingguo
A1 Zhuge, Jianwei
PB ACM
YR 2022
K1 Binary Code Similarity
K1 Graph Neural Network
K1 IoT Software Supply Chain
K1 Vulnerability Detection
K1 Security and privacy
K1 Software and application security
K1 Software security engineering
K1 Mathematics of computing
K1 Discrete mathematics
K1 Graph theory
K1 Graph algorithms
K1 Computing methodologies
K1 Machine learning
K1 Learning paradigms
K1 Supervised learning
K1 Supervised learning by classification
K1 Web application security
SP 205
OP 215
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3544902.3546240
DO https://dl.acm.org/doi/10.1145/3544902.3546240
SF ELIB - SuUB Bremen

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