Robustness of Image-based Android Malware Detection Under Adversarial Attacks

Darwaish, Asim; Naït‐Abdesselam, Farid; Titouna, Chafiq; Sattar, Sumera

doi:10.1109/icc42927.2021.9500425

Cited by 10 publications

(1 citation statement)

References 11 publications

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“…Other similar break-and-fix studies of various Android malware detection models, such as the ones based on Heterogeneous-Graph-based models (Hou et al 2019) or Image-based classification (Darwaish et al 2021) has been performed as well. Such insights can inform the design in the model training stage of the pipelines, to make them more robust to adversarial attacks.…”

Section: • Receiver Operating Characteristic (Roc) Curvementioning

confidence: 99%

On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities

et al. 2022

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As the smartphone market leader, Android has been a prominent target for malware attacks. The number of malicious applications (apps) identified for it has increased continually over the past decade, creating an immense challenge for all parties involved. For market holders and researchers, in particular, the large number of samples has made manual malware detection unfeasible, leading to an influx of research that investigate Machine Learning (ML) approaches to automate this process. However, while some of the proposed approaches achieve high performance, rapidly evolving Android malware has made them unable to maintain their accuracy over time. This has created a need in the community to conduct further research, and build more flexible ML pipelines. Doing so, however, is currently hindered by a lack of systematic overview of the existing literature, to learn from and improve upon the existing solutions. Existing survey papers often focus only on parts of the ML process (e.g., data collection or model deployment), while omitting other important stages, such as model evaluation and explanation. In this paper, we address this problem with a review of 42 highly-cited papers, spanning a decade of research (from 2011 to 2021). We introduce a novel procedural taxonomy of the published literature, covering how they have used ML algorithms, what features they have engineered, which dimensionality reduction techniques they have employed, what datasets they have employed for training, and what their evaluation and explanation strategies are. Drawing from this taxonomy, we also identify gaps in knowledge and provide ideas for improvement and future work.

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Section: • Receiver Operating Characteristic (Roc) Curvementioning

confidence: 99%

On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities

et al. 2022

View full text Add to dashboard Cite

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Certifying Accuracy, Privacy, and Robustness of ML-Based Malware Detection

Bena,

Anisetti,

Gianini

et al. 2024

SN COMPUT. SCI.

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Recent advances in artificial intelligence (AI) are radically changing how systems and applications are designed and developed. In this context, new requirements and regulations emerge, such as the AI Act, placing increasing focus on strict non-functional requirements, such as privacy and robustness, and how they are verified. Certification is considered the most suitable solution for non-functional verification of modern distributed systems, and is increasingly pushed forward in the verification of AI-based applications. In this paper, we present a novel dynamic malware detector driven by the requirements in the AI Act, which goes beyond standard support for high accuracy, and also considers privacy and robustness. Privacy aims to limit the need of malware detectors to examine the entire system in depth requiring administrator-level permissions; robustness refers to the ability to cope with malware mounting evasion attacks to escape detection. We then propose a certification scheme to evaluate non-functional properties of malware detectors, which is used to comparatively evaluate our malware detector and two representative deep-learning solutions in literature.

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Machine Learning for Android Malware Detection: Mission Accomplished? A Comprehensive Review of Open Challenges and Future Perspectives

Guerra-Manzanares

2024

Computers & Security

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Robustness of Image-based Android Malware Detection Under Adversarial Attacks

Cited by 10 publications

References 11 publications

On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities

On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities

Certifying Accuracy, Privacy, and Robustness of ML-Based Malware Detection

Machine Learning for Android Malware Detection: Mission Accomplished? A Comprehensive Review of Open Challenges and Future Perspectives

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