Explaining Android Application Authorship Attribution Based on Source Code Analysis

Murenin, Ivan; Novikova, Evgenia; Ushakov, Roman; Kholod, Ivan

doi:10.1007/978-3-030-65726-0_5

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…LIME is considered model-agnostic, meaning it can be applied to any model, and it aims to provide locally faithful interpretations. Murenin et al [175] utilized the LIME technique and proposed a solution that could display the most and the least used keywords and features, so as to identify the authorship in Android applications and provide the explanations for the attribution decisions made by classifiers.…”

Section: Explanation Of Attribution Through Featuresmentioning

confidence: 99%

Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey

He,

Lashkari,

Vombatkere

et al. 2024

Information

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Over the past few decades, researchers have put their effort and paid significant attention to the authorship attribution field, as it plays an important role in software forensics analysis, plagiarism detection, security attack detection, and protection of trade secrets, patent claims, copyright infringement, or cases of software theft. It helps new researchers understand the state-of-the-art works on authorship attribution methods, identify and examine the emerging methods for authorship attribution, and discuss their key concepts, associated challenges, and potential future work that could help newcomers in this field. This paper comprehensively surveys authorship attribution methods and their key classifications, used feature types, available datasets, model evaluation criteria and metrics, and challenges and limitations. In addition, we discuss the potential future research directions of the authorship attribution field based on the insights and lessons learned from this survey work.

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Section: Explanation Of Attribution Through Featuresmentioning

confidence: 99%

Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey

He,

Lashkari,

Vombatkere

et al. 2024

Information

View full text Add to dashboard Cite

show abstract

“…They also note that a key research challenge is feature selection that captures author's style rather than functionality of the program. Following this, Murenin et al [80] have used LIME to understand the role of selected features in source code attribution for Android malware.…”

Section: Malware Author Attributionmentioning

confidence: 99%

Intelligent Malware Defenses

Nadeem

Rimmer

Joosen

et al. 2022

Security and Artificial Intelligence

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With rapidly evolving threat landscape surrounding malware, intelligent defenses based on machine learning are paramount. In this chapter, we review the literature proposed in the past decade and identify the state-of-the-art in various related research directionsmalware detection, malware analysis, adversarial malware, and malware author attribution. We discuss challenges that emerge when machine learning is applied to malware. We also identify the key issues that need to be addressed by the research community in order to further deepen and systematize research in the malware domain.

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Explaining Android Application Authorship Attribution Based on Source Code Analysis

Cited by 2 publications

References 7 publications

Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey

Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey

Intelligent Malware Defenses

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