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      MDiET: Malware Detection in Encrypted Traffic

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      6th International Symposium for ICS & SCADA Cyber Security Research 2019 (ICS-CSR)

      Cyber Security Research

      10th-12th September 2019

      malware, machine learning, supervised learning, IoT, mobile networks, industrial automation

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          Abstract

          With the increasing adoption of end-to-end encryption in industrial systems, the risk of distributing hidden malware by exploiting encrypted channels gradually turns to a major concern. Due to encryption, the state-of-the-art, signature-based mechanisms might fail to detect malware sufficiently, thus new approaches are required. In this work, a method for malware detection in encrypted traffic based on Machine Learning is presented. A supervised learning approach is adopted and the efficiency of the solution is demonstrated by a set of exhaustive simulations. Further considerations for incorporating the proposed method in a reference industrial network are also discussed.

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          Identifying Encrypted Malware Traffic with Contextual Flow Data

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            Author and article information

            Contributors
            Conference
            September 2019
            September 2019
            : 31-37
            Affiliations
            Nokia Bell Labs

            Cyber Security Research

            Werinherstr. 91, 81541

            Munich, Germany
            Nokia Bell Labs

            Data Science

            Werinherstr. 91, 81541

            Munich, Germany
            Article
            10.14236/ewic/icscsr19.4
            © Dimitrios Schoinianakis et al. Published by BCS Learning and Development Ltd. 6th International Symposium for ICS & SCADA Cyber Security Research 2019

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            6th International Symposium for ICS & SCADA Cyber Security Research 2019
            ICS-CSR
            6
            Athens, Greece
            10th-12th September 2019
            Electronic Workshops in Computing (eWiC)
            Cyber Security Research
            Product
            Product Information: 1477-9358BCS Learning & Development
            Self URI (journal page): https://ewic.bcs.org/
            Categories
            Electronic Workshops in Computing

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