Dr. Kevin Allix
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| Faculty or Centre | Interdisciplinary Centre for Security, Reliability and Trust | ||||
| Department | TruX | ||||
| Postal Address |
Campus Kirchberg, Université du Luxembourg 6, rue Richard Coudenhove-Kalergi L-1359 Luxembourg |
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| Campus Office | E 104 | ||||
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| Telephone | (+352) 46 66 44 5720 | ||||
Kevin ALLIX received his PhD Diploma in 2016, from the University of Luxembourg. He has a working experience in network security devices and his research interests are in Machine-Learning, IT Security and automatic documents processing. Kevin joined the Security, Design and Validation group, SerVal, headed by Prof. Yves Le Traon.
Last updated on: Monday, 20 January 2020
2022
On the Suitability of SHAP Explanations for Refining Classifications; ; ; ; ; ; ; ;
in In Proceedings of the 14th International Conference on Agents and Artificial Intelligence (ICAART 2022) (2022, February)
A Deep Dive inside DREBIN: An Explorative Analysis beyond Android Malware Detection Scores; ; ;
in ACM Transactions on Privacy and Security (2022), 25(2),
LuxemBERT: Simple and Practical Data Augmentation in Language Model Pre-Training for Luxembourgish; ; ; ; ; ; ; ;
in Proceedings of the Language Resources and Evaluation Conference, 2022 (2022, June)
JuCify: A Step Towards Android Code Unification for Enhanced Static Analysis; ; ; ; ; ; ; ;
in 44th International Conference on Software Engineering (ICSE 2022) (2022, May 21)
2021
A Comparison of Pre-Trained Language Models for Multi-Class Text Classification in the Financial Domain; ; ; ; ; ;
in Companion Proceedings of the Web Conference 2021 (WWW '21 Companion), April 19--23, 2021, Ljubljana, Slovenia (2021, April 19)
Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection; ; ;
in Empirical Software Engineering (2021), 26
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection Based on Image Representation of Bytecode; ; ; ; ;
in Communications in Computer and Information Science (2021)
Comparing MultiLingual and Multiple MonoLingual Models for Intent Classification and Slot Filling; ; ; ; ;
in 26th International Conference on Applications of Natural Language to Information Systems (2021, June 25)
Revisiting the VCCFinder approach for the identification of vulnerability-contributing commits; ; ; ; ;
in Empirical Software Engineering (2021), 26
A First Look at Android Applications in Google Play related to Covid-19; ; ;
in Empirical Software Engineering (2021)
Android Malware Detection: Looking beyond Dalvik Bytecode; ; ;
in 2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) (2021, November 15)
2020
Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition; ; ; ;
in Proceedings of the 28th International Conference on Computational Linguistics (2020, December)
Challenges Towards Production-Ready Explainable Machine Learning; ; ; ;
in Veiber, Lisa; Allix, Kevin; Arslan, Yusuf; Bissyande, Tegawendé François D Assise; Klein, Jacques (Eds.) Proceedings of the 2020 USENIX Conference on Operational Machine Learning (OpML 20) (2020, July)
2016
AndroZoo: Collecting Millions of Android Apps for the Research Community; ; ;
in Proceedings of the 13th International Workshop on Mining Software Repositories (2016, May)
On the Lack of Consensus in Anti-Virus Decisions: Metrics and Insights on Building Ground Truths of Android Malware; ; ; ;
in Detection of Intrusions and Malware, and Vulnerability Assessment - 13th International Conference (2016)
2015
Challenges and Outlook in Machine Learning-based Malware Detection for AndroidDoctoral thesis (2015)
Are Your Training Datasets Yet Relevant? - An Investigation into the Importance of Timeline in Machine Learning-Based Malware Detection; ; ;
in Engineering Secure Software and Systems - 7th International Symposium ESSoS 2015, Milan, Italy, March 4-6, 2015. Proceedings (2015)
Potential Component Leaks in Android Apps: An Investigation into a new Feature Set for Malware Detection; ; ; ; ;
in The 2015 IEEE International Conference on Software Quality, Reliability and Security (QRS 2015) (2015, August)
2014
Empirical assessment of machine learning-based malware detectors for Android: Measuring the Gap between In-the-Lab and In-the-Wild Validation Scenarios; ; ; ; ;
in Empirical Software Engineering (2014)
Large-scale Machine Learning-based Malware Detection: Confronting the "10-fold Cross Validation" Scheme with Reality; ; ; ; ;
in Proceedings of the 4th ACM Conference on Data and Application Security and Privacy (2014, March)
A Forensic Analysis of Android Malware -- How is Malware Written and How It Could Be Detected?; ; ; ; ;
in Proceedings of the 2014 IEEE 38th Annual Computer Software and Applications Conference (2014, July)
Using opcode-sequences to detect malicious Android applications; ; ;
in IEEE International Conference on Communications, ICC 2014, Sydney Australia, June 10-14, 2014 (2014, June)
2012
Improving Privacy on Android Smartphones Through In-Vivo Bytecode Instrumentation; ; ; ;
Report (2012)
In-Vivo Bytecode Instrumentation for Improving Privacy on Android Smartphones in Uncertain Environments; ; ; ;
E-print/Working paper (2012)
