PCAPVision: PCAP-Based High-Velocity and Large-Volume Network Failure Detection

Author:

Tulczyjew Lukasz1ORCID,Biruk Ihor2ORCID,Bilgic Murat3ORCID,Abondo Charles4ORCID,Weill Nathanael4ORCID

Affiliation:

1. B-Yond, Warsaw, Poland

2. B-Yond, Wrocław, Poland

3. B-Yond, Redmond, Washington, USA

4. B-Yond, Montreal, Quebec, Canada

Publisher

ACM

Reference22 articles.

1. When a RF beats a CNN and GRU, together—A comparison of deep learning and classical machine learning approaches for encrypted malware traffic classification

2. L. Tulczyjew, K. Jarrah, C. Abondo, D. Bennett, and N. Weill, "LLMcap: Large Language Model for Unsupervised PCAP Failure Detection," in Workshop on the Impact of Large Language Models on 6G Networks (ICC), Jun 2024. Accepted.

3. W. Wang, M. Zhu, X. Zeng, X. Ye, and Y. Sheng, "Malware traffic classification using convolutional neural network for representation learning," in 2017 International conference on information networking (ICOIN), pp. 712--717, IEEE, 2017.

4. F. Hussain, S. G. Abbas, M. Husnain, U. U. Fayyaz, F. Shahzad, and G. A. Shah, "Iot dos and ddos attack detection using resnet," in 2020 IEEE 23rd International Multitopic Conference (INMIC), pp. 1--6, IEEE, 2020.

5. G. Agrafiotis, E. Makri, I. Flionis, A. Lalas, K. Votis, and D. Tzovaras, "Image-based neural network models for malware traffic classification using pcap to picture conversion," in Proceedings of the 17th International Conference on Availability, Reliability and Security, pp. 1--7, 2022.

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