DGA botnet detection method based on capsule network and k-means routing-Reference-Cited by-同舟云学术

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DGA botnet detection method based on capsule network and k-means routing

Published:2022-01-31 Issue:11 Volume:34 Page:8803-8821
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Liu Xiaoyang,Liu Jiamiao^ORCID

Funder

National Office for Philosophy and Social Sciences

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-022-06904-3.pdf

Reference38 articles.

1. Holgado P, Villagrá VA, Vazquez L (2017) Real-time multistep attack prediction based on hidden markov models. IEEE Trans Depend Secure Comput 17(1):134–147

2. Yun X, Huang J, Wang Y, Zang T, Zhou Y, Zhang Y (2019) Khaos: An adversarial neural network DGA with high anti-detection ability. IEEE Trans Inf Forensics Secur 15:2225–2240

3. Stone-Gross B, Cova M, Cavallaro L, Gilbert B, Szydlowski M, Kemmerer R, Vigna G (2009) Your botnet is my botnet: analysis of a botnet takeover. In: Proceedings of the 16th ACM conference on Computer and communications security. pp 635–647

4. Yin L, Luo X, Zhu C, Wang L, Xu Z, Lu H (2019) ConnSpoiler: disrupting C&C communication of IoT-based botnet through fast detection of anomalous domain queries. IEEE Trans Industr Inf 16(2):1373–1384

5. Vinayakumar R, Alazab M, Srinivasan S, Pham QV, Padannayil SK, Simran K (2020) A visualized botnet detection system based deep learning for the internet of things networks of smart cities. IEEE Trans Ind Appl 56(4):4436–4456

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1. A DGA Domain Name Detection Method Based on Two-Stage Feature Reinforcement;2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom);2023-11-01

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