MalDIST: From Encrypted Traffic Classification to Malware Traffic Detection and Classification

Ofek Bader; Adi Lichy; Chen Hajaj; Ran Dubin; Amit Dvir

doi:10.1109/CCNC49033.2022.9700625

MalDIST: From Encrypted Traffic Classification to Malware Traffic Detection and Classification

Ofek Bader, Adi Lichy, Chen Hajaj, Ran Dubin, Amit Dvir

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

23 Scopus citations

Abstract

The world of malware is shifting towards using encrypted traffic. While encryption improves the privacy of users, it brings challenges in the fields of QoS, QoE, and cybersecurity. Recent state-of-the-art Deep-Learning architectures for encrypted traffic classifications demonstrated superb results in tasks of traffic categorization over encrypted traffic. In this paper, we leverage the feasibility to use such architectures for the tasks of malware detection and classification to gain insights into how well these architectures perform in the domain of malware traffic. Specifically, we present a Deep-Learning model for malware traffic detection and classification (MalDIST), which outperforms both classical ML and DL malware traffic classification models both in terms of detection and classification.

Original language	English
Title of host publication	2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC)
Pages	527-533
Number of pages	7
ISBN (Electronic)	978-1-4673-9292-1
DOIs	https://doi.org/10.1109/CCNC49033.2022.9700625
State	Published - 2022
Event	19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022 - Virtual, Online, United States Duration: 8 Jan 2022 → 11 Jan 2022

Publication series

Name	Proceedings - IEEE Consumer Communications and Networking Conference, CCNC
ISSN (Print)	2331-9860

Conference

Conference	19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022
Country/Territory	United States
City	Virtual, Online
Period	8/01/22 → 11/01/22

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10.1109/CCNC49033.2022.9700625

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Bader, O., Lichy, A., Hajaj, C., Dubin, R., & Dvir, A. (2022). MalDIST: From Encrypted Traffic Classification to Malware Traffic Detection and Classification. In 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC) (pp. 527-533). (Proceedings - IEEE Consumer Communications and Networking Conference, CCNC). https://doi.org/10.1109/CCNC49033.2022.9700625

@inproceedings{6cf98c97b49642589961d8d14a7ca82b,

title = "MalDIST: From Encrypted Traffic Classification to Malware Traffic Detection and Classification",

abstract = "The world of malware is shifting towards using encrypted traffic. While encryption improves the privacy of users, it brings challenges in the fields of QoS, QoE, and cybersecurity. Recent state-of-the-art Deep-Learning architectures for encrypted traffic classifications demonstrated superb results in tasks of traffic categorization over encrypted traffic. In this paper, we leverage the feasibility to use such architectures for the tasks of malware detection and classification to gain insights into how well these architectures perform in the domain of malware traffic. Specifically, we present a Deep-Learning model for malware traffic detection and classification (MalDIST), which outperforms both classical ML and DL malware traffic classification models both in terms of detection and classification.",

author = "Ofek Bader and Adi Lichy and Chen Hajaj and Ran Dubin and Amit Dvir",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022 ; Conference date: 08-01-2022 Through 11-01-2022",

year = "2022",

doi = "10.1109/CCNC49033.2022.9700625",

language = "אנגלית",

series = "Proceedings - IEEE Consumer Communications and Networking Conference, CCNC",

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booktitle = "2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC)",

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Bader, O, Lichy, A, Hajaj, C , Dubin, R & Dvir, A 2022, MalDIST: From Encrypted Traffic Classification to Malware Traffic Detection and Classification. in 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC). Proceedings - IEEE Consumer Communications and Networking Conference, CCNC, pp. 527-533, 19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022, Virtual, Online, United States, 8/01/22. https://doi.org/10.1109/CCNC49033.2022.9700625

MalDIST: From Encrypted Traffic Classification to Malware Traffic Detection and Classification. / Bader, Ofek; Lichy, Adi; Hajaj, Chen et al.
2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC). 2022. p. 527-533 (Proceedings - IEEE Consumer Communications and Networking Conference, CCNC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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MalDIST: From Encrypted Traffic Classification to Malware Traffic Detection and Classification

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