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: Contribution to journal › Conference article › peer-review

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
Pages (from-to)	527-533
Number of pages	7
Journal	Proceedings - IEEE Consumer Communications and Networking Conference, CCNC
DOIs	https://doi.org/10.1109/ccnc49033.2022.9700625
State	Published - 1 Jan 2022
Externally published	Yes
Event	19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022 - Virtual, Online, United States Duration: 8 Jan 2022 → 11 Jan 2022

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

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10.1109/ccnc49033.2022.9700625

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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",

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AU - Bader, Ofek

AU - Lichy, Adi

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AU - Dubin, Ran

AU - Dvir, Amit

PY - 2022/1/1

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AB - 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.

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M3 - مقالة من مؤنمر

SN - 2331-9860

SP - 527

EP - 533

JO - Proceedings - IEEE Consumer Communications and Networking Conference, CCNC

JF - Proceedings - IEEE Consumer Communications and Networking Conference, CCNC

Y2 - 8 January 2022 through 11 January 2022

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

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All Science Journal Classification (ASJC) codes

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