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Auflistung nach Schlagwort "Abstract Syntax Tree"

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  • Zeitschriftenartikel
    A Change Impact Analysis Case Study: Replacing the Input Data Model of SoMoX
    (Softwaretechnik-Trends: Vol. 33, No. 2, 2013) Klatt, Benjamin; Küster, Martin; Krogmann, Klaus; Burkhardt, Oliver
    Benjamin Klatt, Martin K¨ uster, Klaus Krogmann, Oliver Burkhardt FZI Research Center for Information Technology Haid-und-Neu-Str. 10-14, 76131 Karlsruhe, Germany {klatt,kuester,krogmann,burkhardt}@fzi.de 1 Introduction
  • Zeitschriftenartikel
    Model-Driven Product Consolidation into Software Product Lines
    (Softwaretechnik-Trends: Vol. 32, No. 2, 2012) Klatt, Benjamin; Krogmann, Klaus
    Benjamin Klatt, Klaus Krogmann FZI Forschungszentrum Informatik Haid-und-Neu-Str. 10-14, 76131 Karlsruhe, Germany {klatt,krogmann}@fzi.de 1
  • Zeitschriftenartikel
    A Toolchain for Metrics-based Comparison of COBOL and Migrated Java Systems
    (Softwaretechnik-Trends: Vol. 32, No. 2, 2012) Jelschen, Jan; Winter, Andreas
    Jan Jelschen, Andreas Winter Carl von Ossietzky Universit¨ at, Oldenburg, Germany {jelschen,winter}@se.uni-oldenburg.de Abstract
  • Textdokument
    Towards Detection of Malicious Software Packages Through Code Reuse by Malevolent Actors
    (GI SICHERHEIT 2022, 2022) Ohm, Marc; Kempf, Lukas; Boes, Felix; Meier, Michael
    Trojanized software packages used in software supply chain attacks constitute an emerging threat. Unfortunately, there is still a lack of scalable approaches that allow automated and timely detection of malicious software packages and thus most detections are based on manual labor and expertise. However, it has been observed that most attack campaigns comprise multiple packages that share the same or similar malicious code. We leverage that fact to automatically reproduce manually identified clusters of known malicious packages that have been used in real world attacks, thus, reducing the need for expert knowledge and manual inspection. Our approach, AST Clustering using MCL to mimic Expertise (ACME), yields promising results with a F1 score of 0.99. Signatures are automatically generated based on characteristic code fragments from clusters and are subsequently used to scan the whole npm registry for unreported malicious packages. We are able to identify and report six malicious packages that have been removed from npm consequentially. Therefore, our approach can support the detection by reducing manual labor and hence may be employed by maintainers of package repositories to detect possible software supply chain attacks through trojanized software packages.