A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks

By Siegfried Rasthofer, Steven Arzt, Eric Bodden. Network and Distributed System Security Symposium, 2014

357 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/33987e58b45755a6120905ce582fc9cbec434763

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Citations in the corpus

Information Flow Analysis of Android Applications in DroidSafe (2015, methodology): “SuSi [24] employs supervised machine learning to automatically designate source and sink methods in the Android API.”
FeatureSmith: Automatically Engineering Features for Malware Detection by Mining the Security Literature (2016, methodology): “FeatureSmith also identifies onNmeaReceiced and onLocationChanged, which could potentially leak location data [37], and isMusicActive, which can be used to infer the user’s location [52].”
A Survey of Android Malware Detection with Deep Neural Models (2020, methodology): “Second, SUSI [108] was applied to transform the sink and source methods into the category level, where there are 19 sink categories and 17 source categories in total.”
Dynamic Malware Analysis in the Modern Era—A State of the Art Survey (2019, methodology): “A machine-learning approach for classifying and categorizing android sources and sinks [96] Rasthofer, 2014 .”
Static analysis of android apps: A systematic literature review (2017, methodology): “SUSI [168]), sensitive data leaks are reported if and only if the data are obtained from source methods (i.”
FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps (2014, background): “FLOWDROID is configured with sources and sinks inferred by our SuSi project [30], by far the most comprehensive one available.”
The Evolution of Android Malware and Android Analysis Techniques (2017, background): “More recently, short for Sources and Sinks (SUSI) [Rasthofer et al. 2014], built on Android v4.”
DL-Droid: Deep learning based android malware detection using real devices (2019, background): “Arp et al. (2014) ; Dini et al. (2012) ; Peiravian and Zhu (2013) ; Rasthofer et al. (2014) ; Shabtai et al. (2012) ; Yerima et al. (2015b, 2016b) are examples of published research that apply machine learning techniques to detect zero-day Android malware.”
IccTA: Detecting Inter-Component Privacy Leaks in Android Apps (2015, methodology): “In this paper, the sources and sinks we use are provided by SUSI [38].”