Structural detection of android malware using embedded call graphs

By Hugo Gascón, Fabian Yamaguchi, Daniel Arp, Konrad Rieck. 2013

349 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Software, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Software

In Signal Processing

Citations in the corpus

Attacking Graph-based Classification via Manipulating the Graph Structure (2019, background): “For example, several approaches [13, 24] proposed to analyze and classify control-flow graphs for malware detection.”
MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (2016, methodology): “Droidmat [57] uses API call tracing and manifest ﬁles to learn features for malware detection, while Gascon et al. [24] rely on embedded call graphs.”
Static analysis of android apps: A systematic literature review (2017, background): “DEX ASSEMBLER Adagio [65], AndRadar [72], Androguard2 [74], Androguard [76], AQUA [142], Brahmastra [146], Brox [103], ComDroid [117], CryptoLint [54], Dflow+DroidInfer [132], Lin et al.”
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection (2018, methodology): “[14] developed a method based on efficient embeddings of function call graphs with an explicit feature map.”
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (2018, methodology): “Machine learning has become a standard tool for malware research in the academic security community: it has been used in a wide range of domains including Windows malware [9, 28, 41], PDF malware [22, 26], malicious URLs [23, 39], Javascript malicious code [8, 35], and Android malware [4, 10, 15, 27”
MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention (2018, methodology): “[37] proposes a method for malware detection based on embeddings of function call graphs in a vector space capturing structural relationship.”
Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs (2014, background): “[17] also extended graph-based malware analysis to the Android environment.”
Contextual Weisfeiler-Lehman graph kernel for malware detection (2016, methodology): “However, higher level semantic representations such as call graphs, control-and data-ﬂow graphs, control, data-and program-dependency graphs mostly stay similar even when the code is considerably altered [3, 5, 7, 8].”
Apposcopy: semantics-based detection of Android malware through static analysis (2014, background): “Many recent malware detectors, such as [8, 22, 10, 33, 6], use machine learning to detect zero-day malware.”
DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket (2014, methodology): “Similarly, the methods Crowdroid [4], DroidMat [36], Adagio [20], MAST [5], and DroidAPIMiner [1] analyze features statically extracted from Android applications using machine learning techniques.”
A Survey of Android Malware Detection with Deep Neural Models (2020, methodology): “The bytecode was extracted from the disassembled classes.dex file before the original instructions were replaced with 15 instruction categories according to Reference [97].”
Modeling and Discovering Vulnerabilities with Code Property Graphs (2014, background): “, to detect variants of known malicious applications [11] and to guide fuzz testing tools [37].”