MMCo: using multimodal deep learning to detect malicious traffic with noisy labels

Qingjun YUAN, Gaopeng GOU, Yuefei ZHU, Yongjuan WANG

PDF(1786 KB)
PDF(1786 KB)
Front. Comput. Sci. ›› 2024, Vol. 18 ›› Issue (1) : 181809. DOI: 10.1007/s11704-023-2386-4
LETTER

MMCo: using multimodal deep learning to detect malicious traffic with noisy labels

Author information +
History +

Graphical abstract

Cite this article

Download citation ▾
Qingjun YUAN, Gaopeng GOU, Yuefei ZHU, Yongjuan WANG. MMCo: using multimodal deep learning to detect malicious traffic with noisy labels. Front. Comput. Sci., 2024, 18(1): 181809 https://doi.org/10.1007/s11704-023-2386-4

References

[1]
Sun X, Ma S, Li Y, Wang D, Li Z, Wang N, Gui G . Enhanced echo-state restricted Boltzmann machines for network traffic prediction. IEEE Internet of Things Journal, 2020, 7( 2): 1287–1297
[2]
Popoola S I, Ande R, Adebisi B, Gui G, Hammoudeh M, Jogunola O . Federated deep learning for zero-day botnet attack detection in IoT-edge devices. IEEE Internet of Things Journal, 2022, 9( 5): 3930–3944
[3]
Ring M, Wunderlich S, Scheuring D, Landes D, Hotho A . A survey of network-based intrusion detection data sets. Computers & Security, 2019, 86: 147–167
[4]
Han B, Yao Q, Yu X, Niu G, Xu M, Hu W, Tsang I W, Sugiyama M. Co-teaching: robust training of deep neural networks with extremely noisy labels. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018, 8536−8546
[5]
Yu X, Han B, Yao J, Niu G, Tsang I W, Sugiyama M. How does disagreement help generalization against label corruption? In: Proceedings of the 36th International Conference on Machine Learning. 2019, 7164−7173
[6]
Tan C, Xia J, Wu L, Li S Z. Co-learning: learning from noisy labels with self-supervision. In: Proceedings of the 29th ACM International Conference on Multimedia. 2021, 1405−1413
[7]
Aceto G, Ciuonzo D, Montieri A, Pescapé A. DISTILLER: encrypted traffic classification via multimodal multitask deep learning. Journal of Network and Computer Applications, 2021, 183−184: 102985
[8]
Nascita A, Montieri A, Aceto G, Ciuonzo D, Persico V, Pescapé A . XAI meets mobile traffic classification: understanding and improving multimodal deep learning architectures. IEEE Transactions on Network and Service Management, 2021, 18( 4): 4225–4246
[9]
Sharafaldin I, Lashkari A H, Ghorbani A A. Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: Proceedings of the 4th International Conference on Information Systems Security and Privacy. 2018, 108−116
[10]
MontazeriShatoori M, Davidson L, Kaur G, Lashkari A H. Detection of DoH tunnels using time-series classification of encrypted traffic. In: Proceedings of the 5th Cyber Science and Technology Congress. 2020, 63−70

Open Access

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

RIGHTS & PERMISSIONS

2023 The Author(s) 2023. This article is published with open access at link.springer.com and journal.hep.com.cn
AI Summary AI Mindmap
PDF(1786 KB)

Accesses

Citations

Detail

Sections
Recommended

/