Publikation: Visual Analytics : Foundations and Experiences in Malware Analysis
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Datum
2018
Autor:innen
Wagner, Markus
Rind, Alexander
Luh, Robert
Schrittwieser, Sebastian
Aigner, Wolfgang
Herausgeber:innen
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Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
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Published
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BEN OTHMANE, Lotfi, ed. and others. Empirical Research for Software Security : Foundations and Experience. Boca Raton: Taylor & Francis, 2018, pp. 139-171. ISBN 978-1-4987-7641-7
Zusammenfassung
This chapter starts by providing some background in behavior-based malware analysis. Subsequently, it introduces VA and its main components based on the knowledge generation model for VA (Sacha et al., 2014). Then, it demonstrates the applicability of VA in in this subfield of software security with three projects that illustrate practical experience of VA methods: MalwareVis (Zhuo et al., 2012) supports network forensics and malware analysis by visually assessing TCP and DNS network streams. SEEM (Gove et al., 2014) allows visual comparison of multiple large attribute sets of malware samples, thereby enabling bulk classification. KAMAS (Wagner et al. 2017) is a knowledge-assisted visualization system for behavior-based malware forensics enabled by API calls and system call traces. Future directions in visual analytics for malware analysis conclude the chapter.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
data, interaction, knowledge generation, malware analysis, model, Visual analytics, Visualization
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WAGNER, Markus, Dominik SACHA, Alexander RIND, Fabian FISCHER, Robert LUH, Sebastian SCHRITTWIESER, Daniel A. KEIM, Wolfgang AIGNER, 2018. Visual Analytics : Foundations and Experiences in Malware Analysis. In: BEN OTHMANE, Lotfi, ed. and others. Empirical Research for Software Security : Foundations and Experience. Boca Raton: Taylor & Francis, 2018, pp. 139-171. ISBN 978-1-4987-7641-7BibTex
@incollection{Wagner2018Visua-41330,
year={2018},
title={Visual Analytics : Foundations and Experiences in Malware Analysis},
isbn={978-1-4987-7641-7},
publisher={Taylor & Francis},
address={Boca Raton},
booktitle={Empirical Research for Software Security : Foundations and Experience},
pages={139--171},
editor={ben Othmane, Lotfi},
author={Wagner, Markus and Sacha, Dominik and Rind, Alexander and Fischer, Fabian and Luh, Robert and Schrittwieser, Sebastian and Keim, Daniel A. and Aigner, Wolfgang}
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<dcterms:abstract xml:lang="eng">This chapter starts by providing some background in behavior-based malware analysis. Subsequently, it introduces VA and its main components based on the knowledge generation model for VA (Sacha et al., 2014). Then, it demonstrates the applicability of VA in in this subfield of software security with three projects that illustrate practical experience of VA methods: MalwareVis (Zhuo et al., 2012) supports network forensics and malware analysis by visually assessing TCP and DNS network streams. SEEM (Gove et al., 2014) allows visual comparison of multiple large attribute sets of malware samples, thereby enabling bulk classification. KAMAS (Wagner et al. 2017) is a knowledge-assisted visualization system for behavior-based malware forensics enabled by API calls and system call traces. Future directions in visual analytics for malware analysis conclude the chapter.</dcterms:abstract>
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