Machine Learning for Malware Analysis in Embedded Systems-Reference-Cited by-同舟云学术

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Machine Learning for Malware Analysis in Embedded Systems

Published:2012-02-24 Issue: Volume: Page:127-153
ISSN:
Container-title:Machine Learning for Embedded System Security
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Author:

Massarelli Luca,Aniello Leonardo,Ciccotelli Claudio,Querzoni Leonardo,Ucci Daniele,Baldoni Roberto

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-94178-9_5

Reference36 articles.

1. Amos, B., Turner, H., White, J.: Applying machine learning classifiers to dynamic android malware detection at scale. In: Proceedings of the 9th international wireless communications and mobile computing conference (IWCMC), pp. 1666–1671 (2013)

2. Arp, D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K., Siemens, C.: Drebin: Effective and explainable detection of android malware in your pocket. In: Proceedings of the 21st Network and Distributed System Security Symposium, (NDSS) (2014)

3. Bickford, J., Lagar-Cavilla, H.A., Varshavsky, A., Ganapathy, V., Iftode, L.: Security versus energy tradeoffs in host-based mobile malware detection. In: Proceedings of the 9th international conference on Mobile systems, applications, and services, pp. 225–238 (2011)

4. Canfora, G., Medvet, E., Mercaldo, F., Visaggio, C.A.: Acquiring and analyzing app metrics for effective mobile malware detection. In: Proceedings of the 2016 International Workshop on Security And Privacy Analytics, pp. 50–57 (2016)

5. Caviglione, L., Gaggero, M., Lalande, J.F., Mazurczyk, W., Urbański, M.: Seeing the unseen: revealing mobile malware hidden communications via energy consumption and artificial intelligence. IEEE Transactions on Information Forensics and Security 11(4), 799–810 (2015)

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1. Malware Detection Using The Machine Learning Based Modified Partial Swarm Optimization Approach;2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC);2023-06-16

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