An efficient eavesdropping model for detection of advanced persistent threat (APT) in high volume network traffic
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16684-0.pdf
Reference57 articles.
1. Marchetti M, Pierazzi F, Colajanni M, Guido A (2016) Analysis of high volumes of network traffic for advanced persistent threat detection. Comput Netw 109:127–141. https://doi.org/10.1016/j.comnet.2016.05.018
2. Panahnejad M, Mirabi M (2022) APT-Dt-KC: advanced persistent threat detection based on kill-chain model. J Supercomput 78(6):8644–8677. https://doi.org/10.1007/s11227-021-04201-9
3. Choi J, Choi C, Lynn H, Kim P (2015) Ontology Based APT Attack Behavior Analysis in Cloud Computing. 2015 10th international conference on broadband and wireless computing. Commun Appl (BWCCA). https://doi.org/10.1109/BWCCA.2015.69
4. Do Xuan C, Dao M, Nguyen H (2020) APT attack detection based on flow network analysis techniques using deep learning. J Intell Fuzzy Syst 39(3):4785–4801. https://doi.org/10.3233/JIFS-200694
5. Moya J, DeCastro-García N, Fernández-Díaz R, Tamargo J (2017) Expert knowledge and data analysis for detecting advanced persistent threats. Open Math 15(1):1108–1122. https://doi.org/10.1515/math-2017-0094
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