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 proceedingConference contributionpeer-review

19 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 languageEnglish
Title of host publication 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC)
Pages527-533
Number of pages7
ISBN (Electronic)978-1-4673-9292-1
DOIs
StatePublished - 2022
Event19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022 - Virtual, Online, United States
Duration: 8 Jan 202211 Jan 2022

Publication series

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

Conference

Conference19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022
Country/TerritoryUnited States
CityVirtual, Online
Period8/01/2211/01/22

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