References of "Klein, Jacques 50002098"
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See detailSensitive and Personal Data: What Exactly Are You Talking About?
Kober, Maria; Samhi, Jordan UL; Arzt, Steven et al

in 10th International Conference on Mobile Software Engineering and Systems 2023 (2023, May)

Mobile devices are pervasively used for a variety of tasks, including the processing of sensitive data in mobile apps. While in most cases access to this data is legitimate, malware often targets ... [more ▼]

Mobile devices are pervasively used for a variety of tasks, including the processing of sensitive data in mobile apps. While in most cases access to this data is legitimate, malware often targets sensitive data and even benign apps collect more data than necessary for their task. Therefore, researchers have proposed several frameworks to detect and track the use of sensitive data in apps, so as to disclose and prevent unauthorized access and data leakage. Unfortunately, a review of the literature reveals a lack of consensus on what sensitive data is in the context of technical frameworks like Android. Authors either provide an intuitive definition or an ad-hoc definition, derive their definition from the Android permission model, or rely on previous research papers which do or do not give a definition of sensitive data. In this paper, we provide an overview of existing definitions of sensitive data in literature and legal frameworks. We further provide a sound definition of sensitive data derived from the definition of personal data of several legal frameworks. To help the scientific community further advance in this field, we publicly provide a list of sensitive sources from the Android framework, thus starting a community project leading to a complete list of sensitive API methods across different frameworks and programming languages. [less ▲]

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See detailNegative Results of Fusing Code and Documentation for Learning to Accurately Identify Sensitive Source and Sink Methods An Application to the Android Framework for Data Leak Detection
Samhi, Jordan UL; Kober, Kober; Kabore, Abdoul Kader UL et al

in 30th IEEE International Conference on Software Analysis, Evolution and Reengineering (2023, March)

Apps on mobile phones manipulate all sorts of data, including sensitive data, leading to privacy related concerns. Recent regulations like the European GDPR provide rules for the processing of personal ... [more ▼]

Apps on mobile phones manipulate all sorts of data, including sensitive data, leading to privacy related concerns. Recent regulations like the European GDPR provide rules for the processing of personal and sensitive data, like that no such data may be leaked without the consent of the user. Researchers have proposed sophisticated approaches to track sensitive data within mobile apps, all of which rely on specific lists of sensitive source and sink methods. The data flow analysis results greatly depend on these lists' quality. Previous approaches either used incomplete hand-written lists and quickly became outdated or relied on machine learning. The latter, however, leads to numerous false positives, as we show. This paper introduces CoDoC that aims to revive the machine-learning approach to precisely identify the privacy-related source and sink API methods. In contrast to previous approaches, CoDoC uses deep learning techniques and combines the source code with the documentation of API methods. Firstly, we propose novel definitions that clarify the concepts of taint analysis, source, and sink methods. Secondly, based on these definitions, we build a new ground truth of Android methods representing sensitive source, sink, and neither methods that will be used to train our classifier. We evaluate CoDoC and show that, on our validation dataset, it achieves a precision, recall, and F1 score of 91%, outperforming the state-of-the-art SuSi. However, similarly to existing tools, we show that in the wild, i.e., with unseen data, CoDoC performs poorly and generates many false-positive results. Our findings suggest that machine-learning models for abstract concepts such as privacy fail in practice despite good lab results. To encourage future research, we release all our artifacts to the community. [less ▲]

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See detailReliable Fix Patterns Inferred from Static Checkers for Automated Program Repair
Liu, Kui; Zhang, Jingtang; Li, Li et al

in ACM Transactions on Software Engineering and Methodology (2023)

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See detailCrex: Predicting patch correctness in automated repair of C programs through transfer learning of execution semantics
Yan, Dapeng; Liu, Kui; Niu, Yuqing et al

in Information and Software Technology (2022), 152

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See detailDemystifying Hidden Sensitive Operations in Android apps
Sun, Xiaoyu; Chen, Xiao; Li, Li et al

in ACM Transactions on Software Engineering and Methodology (2022)

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See detailAssessing the opportunity of combining state-of-the-art Android malware detectors
Daoudi, Nadia UL; Allix, Kévin; Bissyande, Tegawendé François D Assise UL et al

in Empirical Software Engineering (2022), 28

Research on Android malware detection based on Machine learning has been prolific in recent years. In this paper, we show, through a large-scale evaluation of four state-of-the-art approaches that their ... [more ▼]

Research on Android malware detection based on Machine learning has been prolific in recent years. In this paper, we show, through a large-scale evaluation of four state-of-the-art approaches that their achieved performance fluctuates when applied to different datasets. Combining existing approaches appears as an appealing method to stabilise performance. We therefore proceed to empirically investigate the effect of such combinations on the overall detection performance. In our study, we evaluated 22 methods to combine feature sets or predictions from the state-of-the-art approaches. Our results showed that no method has significantly enhanced the detection performance reported by the state-of-the-art malware detectors. Nevertheless, the performance achieved is on par with the best individual classifiers for all settings. Overall, we conduct extensive experiments on the opportunity to combine state-of-the-art detectors. Our main conclusion is that combining state-of-theart malware detectors leads to a stabilisation of the detection performance, and a research agenda on how they should be combined effectively is required to boost malware detection. All artefacts of our large-scale study (i.e., the dataset of ∼0.5 million apks and all extracted features) are made available for replicability. [less ▲]

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See detailA model-based framework for inter-app Vulnerability analysis of Android applications
Nirumand, Atefeh; Zamani, Bahman; Tork-Ladani, Behrouz et al

in Software: Practice and Experience (2022)

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See detailThe Devil is in the Details: Unwrapping the Cryptojacking Malware Ecosystem on Android
Adjibi, Boladji Vinny; Mbodji, Fatou Ndiaye UL; Allix, Kevin et al

in 2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM) (2022, October)

This paper investigates the various technical and non-technical tools and techniques that software developers use to build and disseminate crypto mining apps on Android devices. Our study of 346 potential ... [more ▼]

This paper investigates the various technical and non-technical tools and techniques that software developers use to build and disseminate crypto mining apps on Android devices. Our study of 346 potential Android mining apps, collected between April 2019 and May 2022, has revealed the presence of more than ten mining apps on the Google Play Store, with at least half of those still available at the time of writing this (June 2022). We observed that many of those mining apps do not conceal their usage of the device’s resource for mining which is considered a violation of the store’s policies for developers. We estimate that more than ten thousand users have run mining apps downloaded directly from the Google Play Store, which puts the supposedly ”stringent” vetting process into question. Furthermore, we prove that covert mining apps tend to be embedded into supposedly free versions of premium apps or pose as utility apps that provide valuable features to users. Finally, we empirically demonstrate that cryptojacking apps’ resource consumption and malicious behavior could be insignificant. We presume that typical users, even though they might be running a mobile antivirus solution, could execute a mining app for an extended period without being alerted. We expect our results to inform the various actors involved in the security of Android devices against the lingering threat of cryptojacking and help them better assess the problem. [less ▲]

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See detailTowards Refined Classifications Driven by SHAP Explanations
Arslan, Yusuf UL; Lebichot, Bertrand UL; Allix, Kevin UL et al

in Holzinger, Andreas; Kieseberg, Peter; Tjoa, A. Min (Eds.) et al Machine Learning and Knowledge Extraction (2022, August 11)

Machine Learning (ML) models are inherently approximate; as a result, the predictions of an ML model can be wrong. In applications where errors can jeopardize a company's reputation, human experts often ... [more ▼]

Machine Learning (ML) models are inherently approximate; as a result, the predictions of an ML model can be wrong. In applications where errors can jeopardize a company's reputation, human experts often have to manually check the alarms raised by the ML models by hand, as wrong or delayed decisions can have a significant business impact. These experts often use interpretable ML tools for the verification of predictions. However, post-prediction verification is also costly. In this paper, we hypothesize that the outputs of interpretable ML tools, such as SHAP explanations, can be exploited by machine learning techniques to improve classifier performance. By doing so, the cost of the post-prediction analysis can be reduced. To confirm our intuition, we conduct several experiments where we use SHAP explanations directly as new features. In particular, by considering nine datasets, we first compare the performance of these "SHAP features" against traditional "base features" on binary classification tasks. Then, we add a second-step classifier relying on SHAP features, with the goal of reducing false-positive and false-negative results of typical classifiers. We show that SHAP explanations used as SHAP features can help to improve classification performance, especially for false-negative reduction. [less ▲]

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See detailSSPCATCHER: Learning to catch security patches
Sawadogo, Delwende Arthur; Bissyande, Tegawendé François D Assise UL; Moha, Naouel et al

in Empirical Software Engineering (2022), 27

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See detailDigBug—Pre/post-processing operator selection for accurate bug localization
Kim, Kisub; Ghatpande, Sankalp UL; Liu, Kui et al

in Journal of Systems and Software (2022), 189

Bug localization is a recurrent maintenance task in software development. It aims at identifying relevant code locations (e.g., code files) that must be inspected to fix bugs. When such bugs are reported ... [more ▼]

Bug localization is a recurrent maintenance task in software development. It aims at identifying relevant code locations (e.g., code files) that must be inspected to fix bugs. When such bugs are reported by users, the localization process become often overwhelming as it is mostly a manual task due to incomplete and informal information (written in natural languages) available in bug reports. The research community has then invested in automated approaches, notably using Information Retrieval techniques. Unfortunately, reported performance in the literature is still limited for practical usage. Our key observation, after empirically investigating a large dataset of bug reports as well as workflow and results of state-of-the-art approaches, is that most approaches attempt localization for every bug report without considering the different characteristics of the bug reports. We propose DigBug as a straightforward approach to specialized bug localization. This approach selects pre/post-processing operators based on the attributes of bug reports; and the bug localization model is parameterized in accordance as well. Our experiments confirm that departing from “one-size-fits-all” approaches, DigBug outperforms the state-of-the-art techniques by 6 and 14 percentage points, respectively in terms of MAP and MRR on average. [less ▲]

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See detailLuxemBERT: Simple and Practical Data Augmentation in Language Model Pre-Training for Luxembourgish
Lothritz, Cedric UL; Lebichot, Bertrand UL; Allix, Kevin UL et al

in Proceedings of the Language Resources and Evaluation Conference, 2022 (2022, June)

Pre-trained Language Models such as BERT have become ubiquitous in NLP where they have achieved state-of-the-art performance in most NLP tasks. While these models are readily available for English and ... [more ▼]

Pre-trained Language Models such as BERT have become ubiquitous in NLP where they have achieved state-of-the-art performance in most NLP tasks. While these models are readily available for English and other widely spoken languages, they remain scarce for low-resource languages such as Luxembourgish. In this paper, we present LuxemBERT, a BERT model for the Luxembourgish language that we create using the following approach: we augment the pre-training dataset by considering text data from a closely related language that we partially translate using a simple and straightforward method. We are then able to produce the LuxemBERT model, which we show to be effective for various NLP tasks: it outperforms a simple baseline built with the available Luxembourgish text data as well the multilingual mBERT model, which is currently the only option for transformer-based language models in Luxembourgish. Furthermore, we present datasets for various downstream NLP tasks that we created for this study and will make available to researchers on request. [less ▲]

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See detailTriggerZoo: A Dataset of Android Applications Automatically Infected with Logic Bombs
Samhi, Jordan UL; Bissyande, Tegawendé François D Assise UL; Klein, Jacques UL

in 19th International Conference on Mining Software Repositories, Data Showcase, (MSR 2022) (2022, May 23)

Many Android apps analyzers rely, among other techniques, on dynamic analysis to monitor their runtime behavior and detect potential security threats. However, malicious developers use subtle, though ... [more ▼]

Many Android apps analyzers rely, among other techniques, on dynamic analysis to monitor their runtime behavior and detect potential security threats. However, malicious developers use subtle, though efficient, techniques to bypass dynamic analyzers. Logic bombs are examples of popular techniques where the malicious code is triggered only under specific circumstances, challenging comprehensive dynamic analyses. The research community has proposed various approaches and tools to detect logic bombs. Unfortunately, rigorous assessment and fair comparison of state-of-the-art techniques are impossible due to the lack of ground truth. In this paper, we present TriggerZoo, a new dataset of 406 Android apps containing logic bombs and benign trigger-based behavior that we release only to the research community using authenticated API. These apps are real-world apps from Google Play that have been automatically infected by our tool AndroBomb. The injected pieces of code implementing the logic bombs cover a large pallet of realistic logic bomb types that we have manually characterized from a set of real logic bombs. Researchers can exploit this dataset as ground truth to assess their approaches and provide comparisons against other tools. [less ▲]

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See detailDifuzer: Uncovering Suspicious Hidden Sensitive Operations in Android Apps
Samhi, Jordan UL; Li, Li; Bissyande, Tegawendé François D Assise UL et al

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

One prominent tactic used to keep malicious behavior from being detected during dynamic test campaigns is logic bombs, where malicious operations are triggered only when specific conditions are satisfied ... [more ▼]

One prominent tactic used to keep malicious behavior from being detected during dynamic test campaigns is logic bombs, where malicious operations are triggered only when specific conditions are satisfied. Defusing logic bombs remains an unsolved problem in the literature. In this work, we propose to investigate Suspicious Hidden Sensitive Operations (SHSOs) as a step towards triaging logic bombs. To that end, we develop a novel hybrid approach that combines static analysis and anomaly detection techniques to uncover SHSOs, which we predict as likely implementations of logic bombs. Concretely, Difuzer identifies SHSO entry-points using an instrumentation engine and an inter-procedural data-flow analysis. Then, it extracts trigger-specific features to characterize SHSOs and leverages One-Class SVM to implement an unsupervised learning model for detecting abnormal triggers. We evaluate our prototype and show that it yields a precision of 99.02% to detect SHSOs among which 29.7% are logic bombs. Difuzer outperforms the state-of-the-art in revealing more logic bombs while yielding less false positives in about one order of magnitude less time. All our artifacts are released to the community. [less ▲]

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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 detailiBiR: Bug Report driven Fault Injection
Khanfir, Ahmed UL; Koyuncu, Anil; Papadakis, Mike UL et al

in ACM Transactions on Software Engineering and Methodology (2022)

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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 detailOn the Suitability of SHAP Explanations for Refining Classifications
Arslan, Yusuf UL; Lebichot, Bertrand UL; Allix, Kevin UL et al

in In Proceedings of the 14th International Conference on Agents and Artificial Intelligence (ICAART 2022) (2022, February)

In industrial contexts, when an ML model classifies a sample as positive, it raises an alarm, which is subsequently sent to human analysts for verification. Reducing the number of false alarms upstream in ... [more ▼]

In industrial contexts, when an ML model classifies a sample as positive, it raises an alarm, which is subsequently sent to human analysts for verification. Reducing the number of false alarms upstream in an ML pipeline is paramount to reduce the workload of experts while increasing customers’ trust. Increasingly, SHAP Explanations are leveraged to facilitate manual analysis. Because they have been shown to be useful to human analysts in the detection of false positives, we postulate that SHAP Explanations may provide a means to automate false-positive reduction. To confirm our intuition, we evaluate clustering and rules detection metrics with ground truth labels to understand the utility of SHAP Explanations to discriminate false positives from true positives. We show that SHAP Explanations are indeed relevant in discriminating samples and are a relevant candidate to automate ML tasks and help to detect and reduce false-positive results. [less ▲]

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See detailPredicting Patch Correctness Based on the Similarity of Failing Test Cases
Tian, Haoye UL; Li, Yinghua UL; Pian, Weiguo UL et al

in ACM Transactions on Software Engineering and Methodology (2022)

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See detailThe Best of Both Worlds: Combining Learned Embeddings with Engineered Features for Accurate Prediction of Correct Patches
Tian, Haoye UL; Liu, Kui; Li, Yinghua UL et al

in ACM Transactions on Software Engineering and Methodology (2022)

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