OBLIVE: Seamless Code Obfuscation for Java Programs and Android Apps

Pizzolotto, Davide; Fellin, Roberto; Ceccato, Mariano

doi:10.1109/saner.2019.8667982

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…These differences hinder the Android malware detection methods from applying to the Java malware programs. Few works in the literature investigate Java malware detection [46]. Jarhead [47] performs simple static analysis to extract string features and trains machine learning algorithms for Java malware detection.…”

Section: Java Malware Detection Methodsmentioning

confidence: 99%

BejaGNN: behavior-based Java malware detection via graph neural network

Feng

Yang

et al. 2023

J Supercomput

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As a popular platform-independent language, Java is widely used in enterprise applications. In the past few years, language vulnerabilities exploited by Java malware have become increasingly prevalent, which cause threats for multi-platform. Security researchers continuously propose various approaches for fighting against Java malware programs. The low code path coverage and poor execution efficiency of dynamic analysis limit the large-scale application of dynamic Java malware detection methods. Therefore, researchers turn to extracting abundant static features to implement efficient malware detection. In this paper, we explore the direction of capturing malware semantic information by using graph learning algorithms and present BejaGNN (Behavior-based Java malware detection via Graph Neural Network), a novel behavior-based Java malware detection method using static analysis, word embedding technique, and graph neural network. Specifically, BejaGNN leverages static analysis techniques to extract ICFGs (Inter-procedural Control Flow Graph) from Java program files and then prunes these ICFGs to remove noisy instructions. Then, word embedding techniques are adopted to learn semantic representations for Java bytecode instructions. Finally, BejaGNN builds a graph neural network classifier to determine the maliciousness of Java programs. Experimental results on a public Java bytecode benchmark demonstrate that BejaGNN achieves high F 1 98.8% and is superior to existing Java malware detection approaches, which verifies the promise of graph neural network in Java malware detection.

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Section: Java Malware Detection Methodsmentioning

confidence: 99%

BejaGNN: behavior-based Java malware detection via graph neural network

Feng

Yang

et al. 2023

J Supercomput

View full text Add to dashboard Cite

show abstract

“…These differences hinder the Android malware classification methods from applying to the Java malware programs. Few works in the literature investigate Java malware detection [37]. Jarhead [38] performs simple static analysis to extract string features and trains machine-learning algorithms for Java malware detection.…”

Section: Related Workmentioning

confidence: 99%

BejaGNN: Behavior-based Java Malware Detection via Graph Neural Network

Feng

Yang

et al. 2022

Preprint

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As a popular platform-independent language, Java is widely used in enterprise applications. In the past few years, language vulnerabilities exploited by Java malware have become increasingly prevalent, which cause threats for multi-platform. Security researchers continuously propose various approaches for fighting against Java malware programs. However, the presence of complex hidden techniques, such as code obfuscation, makes identifying complicated Java malware become challenging. Therefore, there is an urgent need to develop new approaches for resisting hidden techniques. In this paper, we present BejaGNN, a novel behavior-based Java malware detection method using static analysis, word embedding technique, and graph neural network. Specifically, BejaGNN leverages static analysis techniques to extract ICFGs from Java program files and then prunes these ICFGs to remove noisy instructions. Then, work embedding techniques are adopted to learn semantic representations for Java bytecode instructions. Finally, BejaGNN builds a graph neural network classifier to determine the maliciousness of Java programs. Experimental results on a public Java bytecode benchmark demonstrate that BejaGNN achieves high F1 98.8% and is superior to existing Java malware detection approaches, which verifies the promise of graph neural network in Java malware detection.

show abstract