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

Graphical abstract

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

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The Author(s) 2023. This article is published with open access at link.springer.com and journal.hep.com.cn