Kevin Allix

1.6k total citations · 1 hit paper
26 papers, 900 citations indexed

About

Kevin Allix is a scholar working on Signal Processing, Software and Computer Networks and Communications. According to data from OpenAlex, Kevin Allix has authored 26 papers receiving a total of 900 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Signal Processing, 15 papers in Software and 12 papers in Computer Networks and Communications. Recurrent topics in Kevin Allix's work include Advanced Malware Detection Techniques (21 papers), Software Testing and Debugging Techniques (14 papers) and Network Security and Intrusion Detection (12 papers). Kevin Allix is often cited by papers focused on Advanced Malware Detection Techniques (21 papers), Software Testing and Debugging Techniques (14 papers) and Network Security and Intrusion Detection (12 papers). Kevin Allix collaborates with scholars based in Luxembourg, France and Australia. Kevin Allix's co-authors include Tegawendé F. Bissyandé, Jacques Klein, Yves Le Traon, Radu State, Thomas Engel, Li Li, Alexandre Bartel, Daoyuan Li, Jun Gao and Naouel Moha and has published in prestigious journals such as IEEE Transactions on Software Engineering, Empirical Software Engineering and ACM Transactions on Software Engineering and Methodology.

In The Last Decade

Kevin Allix

25 papers receiving 873 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

AndroZoo

2016 486 citations Kevin Allix, Tegawendé F. Bissyandé et al. Open Repository and Bibliography (University of Luxembourg) profile →

Peers

Kevin Allix

SP

CNC

IS

SOFTW

AI

Xabier Ugarte-Pedrero Spain

Yuetang Deng China

Rahul Pandita United States

Antonio Nappa Spain

Ryan Riley Qatar

Lannan Luo United States

Philippe Charland Canada

Apostolis Zarras Germany

Giancarlo Pellegrino Germany

James Walden United States

Xabier Ugarte-Pedrero Spain

Kevin Allix

677 ×0.9

SP

561 ×1.1

CNC

399 ×0.9

IS

158 ×0.4

SOFTW

284 ×1.5

AI

Citations per year, relative to Kevin Allix Kevin Allix (= 1×) peers Xabier Ugarte-Pedrero

Countries citing papers authored by Kevin Allix

Since Specialization

Citations

This map shows the geographic impact of Kevin Allix's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kevin Allix with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin Allix more than expected).

Fields of papers citing papers by Kevin Allix

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kevin Allix. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kevin Allix. The network helps show where Kevin Allix may publish in the future.

Co-authorship network of co-authors of Kevin Allix

This figure shows the co-authorship network connecting the top 25 collaborators of Kevin Allix. A scholar is included among the top collaborators of Kevin Allix based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kevin Allix. Kevin Allix is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Allix, Kevin, et al.. (2024). AndroZoo: A Retrospective with a Glimpse into the Future. Open Repository and Bibliography (University of Luxembourg). 389–393.

2.

Kim, Kisub, et al.. (2024). Temporal-Incremental Learning for Android Malware Detection. ACM Transactions on Software Engineering and Methodology. 34(4). 1–30. 2 indexed citations

3.

Kim, Kisub, et al.. (2024). DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware. Open Repository and Bibliography (University of Luxembourg). 420–426. 1 indexed citations

4.

Allix, Kevin, Kisub Kim, Xin Zhou, et al.. (2023). DexBERT: Effective, Task-Agnostic and Fine-Grained Representation Learning of Android Bytecode. IEEE Transactions on Software Engineering. 49(10). 4691–4706. 9 indexed citations

5.

Allix, Kevin, et al.. (2023). Guided Retraining to Enhance the Detection of Difficult Android Malware. Open Repository and Bibliography (University of Luxembourg). 1131–1143. 2 indexed citations

6.

Allix, Kevin, et al.. (2022). A Deep Dive Inside DREBIN: An Explorative Analysis beyond Android Malware Detection Scores. ACM Transactions on Privacy and Security. 25(2). 1–28. 22 indexed citations

7.

Bissyandé, Tegawendé F., Naouel Moha, Kevin Allix, et al.. (2022). SSPCatcher: Learning to catch security patches. Empirical Software Engineering. 27(6). 10 indexed citations

8.

Allix, Kevin, et al.. (2022). Assessing the opportunity of combining state-of-the-art Android malware detectors. Empirical Software Engineering. 28(2). 4 indexed citations

9.

Allix, Kevin, et al.. (2022). Exploiting Prototypical Explanations for Undersampling Imbalanced Datasets. Open Repository and Bibliography (University of Luxembourg). 1449–1454. 2 indexed citations

10.

Lebichot, Bertrand, et al.. (2022). On the Suitability of SHAP Explanations for Refining Classifications. Open Repository and Bibliography (University of Luxembourg). 395–402. 2 indexed citations

11.

Bissyandé, Tegawendé F., et al.. (2022). The Devil is in the Details: Unwrapping the Cryptojacking Malware Ecosystem on Android. Open Repository and Bibliography (University of Luxembourg). 153–163. 2 indexed citations

12.

Allix, Kevin, et al.. (2021). Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection. Empirical Software Engineering. 26(4). 16 indexed citations

13.

Allix, Kevin, et al.. (2021). Android Malware Detection: Looking beyond Dalvik Bytecode. Open Repository and Bibliography (University of Luxembourg). 34–39. 13 indexed citations

14.

Allix, Kevin, Tegawendé F. Bissyandé, Jacques Klein, & Yves Le Traon. (2016). AndroZoo. Open Repository and Bibliography (University of Luxembourg). 468–471. 486 indexed citations breakdown →

15.

Allix, Kevin. (2015). Challenges and Outlook in Machine Learning-based Malware Detection for Android. Open Repository and Bibliography (University of Luxembourg). 2 indexed citations

16.

Li, Li, Kevin Allix, Daoyuan Li, et al.. (2015). Potential Component Leaks in Android Apps: An Investigation into a New Feature Set for Malware Detection. Open Repository and Bibliography (University of Luxembourg). 195–200. 22 indexed citations

17.

Allix, Kevin, Tegawendé F. Bissyandé, Jacques Klein, & Yves Le Traon. (2014). Machine Learning-Based Malware Detection for Android Applications: History Matters!. Open Repository and Bibliography (University of Luxembourg). 254(Pt B). 113101–113101. 13 indexed citations

18.

Allix, Kevin, et al.. (2014). A Forensic Analysis of Android Malware -- How is Malware Written and How it Could Be Detected?. Open Repository and Bibliography (University of Luxembourg). 384–393. 31 indexed citations

19.

Allix, Kevin, et al.. (2014). Empirical assessment of machine learning-based malware detectors for Android. Empirical Software Engineering. 21(1). 183–211. 99 indexed citations

20.

Allix, Kevin, et al.. (2014). Using opcode-sequences to detect malicious Android applications. Open Repository and Bibliography (University of Luxembourg). 914–919. 51 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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