References of "Daoudi, Nadia 50035095"
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See detailJuCify: A Step Towards Android Code Unification for Enhanced Static Analysis
Samhi, Jordan UL; Gao, Jun UL; Daoudi, Nadia UL et al

in 44th International Conference on Software Engineering (ICSE 2022) (2022, May 21)

Native code is now commonplace within Android app packages where it co-exists and interacts with Dex bytecode through the Java Native Interface to deliver rich app functionalities. Yet, state-of-the-art ... [more ▼]

Native code is now commonplace within Android app packages where it co-exists and interacts with Dex bytecode through the Java Native Interface to deliver rich app functionalities. Yet, state-of-the-art static analysis approaches have mostly overlooked the presence of such native code, which, however, may implement some key sensitive, or even malicious, parts of the app behavior. This limitation of the state of the art is a severe threat to validity in a large range of static analyses that do not have a complete view of the executable code in apps. To address this issue, we propose a new advance in the ambitious research direction of building a unified model of all code in Android apps. The JuCify approach presented in this paper is a significant step towards such a model, where we extract and merge call graphs of native code and bytecode to make the final model readily-usable by a common Android analysis framework: in our implementation, JuCify builds on the Soot internal intermediate representation. We performed empirical investigations to highlight how, without the unified model, a significant amount of Java methods called from the native code are ``unreachable'' in apps' call-graphs, both in goodware and malware. Using JuCify, we were able to enable static analyzers to reveal cases where malware relied on native code to hide invocation of payment library code or of other sensitive code in the Android framework. Additionally, JuCify's model enables state-of-the-art tools to achieve better precision and recall in detecting data leaks through native code. Finally, we show that by using JuCify we can find sensitive data leaks that pass through native code. [less ▲]

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See detailA Deep Dive inside DREBIN: An Explorative Analysis beyond Android Malware Detection Scores
Daoudi, Nadia UL; Allix, Kevin UL; Bissyande, Tegawendé François D Assise UL et al

in ACM Transactions on Privacy and Security (2022), 25(2),

Machine learning (ML) advances have been extensively explored for implementing large-scale malware detection. When reported in the literature, performance evaluation of ML-based detectors generally ... [more ▼]

Machine learning (ML) advances have been extensively explored for implementing large-scale malware detection. When reported in the literature, performance evaluation of ML-based detectors generally focuses on highlighting the ratio of samples that are correctly or incorrectly classified, overlooking essential questions on why/how the learned models can be demonstrated as reliable. In the Android ecosystem, several recent studies have highlighted how evaluation setups can carry biases related to datasets or evaluation methodologies. Nevertheless, there is little work attempting to dissect the produced model to provide some understanding of its intrinsic characteristics. In this work, we fill this gap by performing a comprehensive analysis of a state-of-the-art Android Malware detector, namely DREBIN, which constitutes today a key reference in the literature. Our study mainly targets an in-depth understanding of the classifier characteristics in terms of (1) which features actually matter among the hundreds of thousands that DREBIN extracts, (2) whether the high scores of the classifier are dependent on the dataset age, (3) whether DREBIN's explanations are consistent within malware families, etc. Overall, our tentative analysis provides insights into the discriminatory power of the feature set used by DREBIN to detect malware. We expect our findings to bring about a systematisation of knowledge for the community. [less ▲]

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See detailAndroid Malware Detection: Looking beyond Dalvik Bytecode
Sun, Tiezhu UL; Daoudi, Nadia UL; Allix, Kevin UL et al

in 2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) (2021, November 15)

Machine learning has been widely employed in the literature of malware detection because it is adapted to the need for scalability in vetting large scale samples of Android. Feature engineering has ... [more ▼]

Machine learning has been widely employed in the literature of malware detection because it is adapted to the need for scalability in vetting large scale samples of Android. Feature engineering has therefore been the key focus for research advances. Recently, a new research direction that builds on the momentum of Deep Learning for computer vision has produced promising results with image representations of Android byte- code. In this work, we postulate that other artifacts such as the binary (native) code and metadata/configuration files could be looked at to build more exhaustive representations of Android apps. We show that binary code and metadata files can also provide relevant information for Android malware detection, i.e., that they can allow to detect Malware that are not detected by models built only on bytecode. Furthermore, we investigate the potential benefits of combining all these artifacts into a unique representation with a strong signal for reasoning about maliciousness. [less ▲]

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See detailDexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection Based on Image Representation of Bytecode
Daoudi, Nadia UL; Samhi, Jordan UL; Kabore, Abdoul Kader UL et al

in Communications in Computer and Information Science (2021)

Computer vision has witnessed several advances in recent years, with unprecedented performance provided by deep representation learning research. Image formats thus appear attractive to other fields such ... [more ▼]

Computer vision has witnessed several advances in recent years, with unprecedented performance provided by deep representation learning research. Image formats thus appear attractive to other fields such as malware detection, where deep learning on images alleviates the need for comprehensively hand-crafted features generalising to different malware variants. We postulate that this research direction could become the next frontier in Android malware detection, and therefore requires a clear roadmap to ensure that new approaches indeed bring novel contributions. We contribute with a first building block by developing and assessing a baseline pipeline for image-based malware detection with straightforward steps. We propose DexRay, which converts the bytecode of the app DEX files into grey-scale “vector” images and feeds them to a 1-dimensional Convolutional Neural Network model. We view DexRay as foundational due to the exceedingly basic nature of the design choices, allowing to infer what could be a minimal performance that can be obtained with image-based learning in malware detection. The performance of DexRay evaluated on over 158k apps demonstrates that, while simple, our approach is effective with a high detection rate(F1-score= 0.96). Finally, we investigate the impact of time decay and image-resizing on the performance of DexRay and assess its resilience to obfuscation. This work-in-progress paper contributes to the domain of Deep Learning based Malware detection by providing a sound, simple, yet effective approach (with available artefacts) that can be the basis to scope the many profound questions that will need to be investigated to fully develop this domain. [less ▲]

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See detailLessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection
Daoudi, Nadia UL; Allix, Kevin UL; Bissyande, Tegawendé François D Assise UL et al

in Empirical Software Engineering (2021), 26

A well-known curse of computer security research is that it often produces systems that, while technically sound, fail operationally. To overcome this curse, the community generally seeks to assess ... [more ▼]

A well-known curse of computer security research is that it often produces systems that, while technically sound, fail operationally. To overcome this curse, the community generally seeks to assess proposed systems under a variety of settings in order to make explicit every potential bias. In this respect, recently, research achievements on machine learning based malware detection are being considered for thorough evaluation by the community. Such an effort of comprehensive evaluation supposes first and foremost the possibility to perform an independent reproduction study in order to sharpen evaluations presented by approaches’ authors. The question Can published approaches actually be reproduced? thus becomes paramount despite the little interest such mundane and practical aspects seem to attract in the malware detection field. In this paper, we attempt a complete reproduction of five Android Malware Detectors from the literature and discuss to what extent they are “reproducible”. Notably, we provide insights on the implications around the guesswork that may be required to finalise a working implementation. Finally, we discuss how barriers to reproduction could be lifted, and how the malware detection field would benefit from stronger reproducibility standards—like many various fields already have. [less ▲]

Detailed reference viewed: 166 (16 UL)