Determining if software is malicious in nature is dependent on the context in which it is used. This work explores the importance of context in malware detection and provides proof-of-concept context-aware models using topic modeling. These models provide insight into the challenges of contextual modeling in software analysis.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Context-Aware Malware Detection Using Topic Modeling


    Beteiligte:
    Stegner, Wayne (Autor:in) / Kapp, David (Autor:in) / Kebede, Temesguen (Autor:in) / Jha, Rashmi (Autor:in)


    Erscheinungsdatum :

    16.08.2021


    Format / Umfang :

    1246777 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Malware Detection using the Context of API Calls

    Chandrasekaran, Monika / Ralescu, Anca / Kapp, David et al. | IEEE | 2021


    Static Analysis through Topic Modeling and its Application to Malware Programs Classification

    Djaneye-Boundjou, Ouboti / Messay-Kebede, Temesguen / Kapp, David et al. | IEEE | 2019


    Context-aware pedestrian detection using LIDAR

    Oliveira, L / Nunes, U | IEEE | 2010


    Context-Aware Pedestrian Detection Using LIDAR, pp. 773-778

    Oliveira, L. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2010


    Augmenting Topic Finding in the NASA Aviation Safety Reporting System using Topic Modeling

    Paradis, Carlos / Kazman, Rick / Davies, Misty et al. | AIAA | 2021