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Auflistung nach Autor:in "Avdiienko, Vitalii"

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  • Konferenzbeitrag
    AppMining
    (Software Engineering 2017, 2017) Avdiienko, Vitalii; Kuznetsov, Konstantin; Gorla, Alessandra; Zeller, Andreas; Arzt, 
Steven; Rasthofer, Siegfried; Bodden, Eric
    A fundamental question of security analysis is: When is a behavior normal, and when is it not? We present techniques that extract behavior patterns from thousands of apps—patters that represent normal behavior, such as “A travel app normally does not access stored text messages”. Combining data flow analysis with app descriptions and GUI data from both apps and their stores allows for massive machine learning, which then also allows to detect yet unknown malware by classifying it as abnormal.
  • Konferenzbeitrag
    Detecting Information Flow by Mutating Input Data
    (Software Engineering und Software Management 2018, 2018) Mathis, Björn; Avdiienko, Vitalii; Soremekun, Ezekiel O.; Böhme, Marcel; Zeller, Andreas
    [Accepted as full paper for ASE 2017] Analyzing information flow is central in assessing the security of applications. However, static and dynamic analyses of information flow are easily challenged by non-available or obscure code. We present a lightweight mutation-based analysis that systematically mutates dynamic values returned by sensitive sources to assess whether the mutation changes the values passed to sensitive sinks. If so, we found a flow between source and sink. In contrast to existing techniques, mutation-based flow analysis does not attempt to identify the specific path of the flow and is thus resilient to obfuscation. In its evaluation, our MUTAFLOW prototype for Android programs showed that mutation-based flow analysis is a lightweight yet effective complement to existing tools. Compared to the popular FLOWDROID static analysis tool, MUTAFLOW requires less than 10% of source code lines but has similar accuracy; on 20 tested real-world apps, it is able to detect 75 flows that FLOWDROID misses.