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On the Feasibility of Supervised Machine Learning for the D..:

, In: Proceedings of the 17th International Conference on Availability, Reliability and Security,

Ohm, Marc ; Boes, Felix ; Bungartz, Christian. - p. 1-10 , 2022

Link: https://dl.acm.org/doi/10.1145/3538969.3544415

RT 
T1 <cTitle>Proceedings of the 17th International Conference on Availability, Reliability and Security</cTitle>: T1 <Title>On the Feasibility of Supervised Machine Learning for the Detection of Malicious Software Packages</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3544415&Exemplar=1&LAN=DE
				
A1 Ohm, Marc
A1 Boes, Felix
A1 Bungartz, Christian
A1 Meier, Michael
PB ACM
YR 2022
K1 Malware Detection
K1 Software Supply Chain
K1 Supervised Machine Learning
K1 Security and privacy
K1 Intrusion/anomaly detection and malware mitigation
K1 Computing methodologies
K1 Machine learning
K1 Learning paradigms
K1 Supervised learning
K1 Supervised learning by classification
K1 Software and its engineering
K1 Software notations and tools
K1 Software libraries and repositories
K1 Information systems
K1 Information systems applications
K1 Collaborative and social computing systems and tools
K1 Open source software
SP 1
OP 10
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3538969.3544415
DO https://dl.acm.org/doi/10.1145/3538969.3544415
SF ELIB - SuUB Bremen

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