Aurore Fass

Are you interested in a 𝗧𝗲𝗻𝘂𝗿𝗲𝗱 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗣𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗮 𝗯𝗮𝗰𝗸𝗴𝗿𝗼𝘂𝗻𝗱 𝗶𝗻 𝗘𝗨 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝘁𝗲𝗰𝘁𝗶𝗼𝗻, 𝗗𝗦𝗔, 𝗗𝗠𝗔 𝗟𝗮𝘄 at Inria? With Nataliia Bielova, we are seeking candidates interested in applying to join our future team!
📅 Express interest by Feb 6, 2026
👉 More info here: www-sop.inria.fr/members/Nata...

(Self-)Nomination for the USENIX Security '26 Artifact Evaluation Committee (AEC) For the seventh year, USENIX Security allows the evaluation of artifacts that support a paper: software, hardware, evaluation data and documentation, raw measurement data, raw survey results, mechaniz...

Last chance to (self-) nominate for USENIX Security'26 Artifact Evaluation Committee!
You should expect a low load of ~1 artifact for functionality/reproducibility assessments per cycle (max 3 for the whole year).

Please support Open Science and fill the form by Oct 17: forms.gle/WoYRX4govNY1... 🚀

You are an Early Career Researcher in #cybersec? Here is an opportunity: The AEC chairs of @USENIXSecurity '26 are looking for (self)nominations for the Artifact Evaluation Committee. Deadline: October 17th, 2025, so sign up soon!
@chwress.bsky.social, @kumarde.bsky.social, @aurore-fass.bsky.social

GitHub - its-not-easy/tweb25: Repository for the paper "It's not Easy: Applying Supervised Machine Learning to Detect Malicious Extensions in the Chrome Web Store" Repository for the paper "It's not Easy: Applying Supervised Machine Learning to Detect Malicious Extensions in the Chrome Web Store" - its-not-easy/tweb25

Repository: github.com/its-not-easy...
Paper: arxiv.org/pdf/2509.21590

Joint work with Ben Rosenzweig, @rossoalfiere.bsky.social, and Giovanni Apruzzese

We also provide ground truth (benign or malicious), category, manifest version, date of last update, and ID of each extension. Put simply, you can just (i) grab the dataset, (ii) load it into a pandas dataframe, and (iii) train & test any type of ML-based classifier on it. Enjoy!

Each extension in our dataset is provided as a *feature vector that is ready for ML-based experiments*. For instance, we provide 2.5k features extracted from extensions’ source code and over 2k features extracted from extensions’ metadata.

For the results, look at the paper (spoiler: the browser- extension ecosystem is strongly impacted by concept drift). For those who want to get their hands dirty, we release all our codebase in our GitHub repository.

In our new TWEB paper, we collected ~100k browser extensions that appeared on the Chrome Web Store in 2012–2023 and used them to develop and test a set of classifiers based on supervised machine learning (ML).

Ever wanted to get into malicious browser-extension research, but couldn't find any dataset to experiment on? We got you covered!

(Self-)Nomination for the USENIX Security '26 Artifact Evaluation Committee (AEC) For the seventh year, USENIX Security allows the evaluation of artifacts that support a paper: software, hardware, evaluation data and documentation, raw measurement data, raw survey results, mechaniz...

We are still looking for (self) nominations for USENIX Security '26 Artifact Evaluation Committee!
Please help us spread the word 🚀

You can *Promote and Support Open Science* by filling out the nomination form by Friday, October 17: forms.gle/sMCJHww2qcUK...

No award in 2025? Here is another chance: @chwress.bsky.social, @kumarde.bsky.social, and I are looking for (self) nominations for @USENIXSecurity '26 Artifact Evaluation Committee!

Remember to fill the nomination form by Friday, Oct 17: forms.gle/sMCJHww2qcUK...

And help us spread the word! 🚀

Congratulations to USENIX Security 2025 AE:
🏆 Distinguished Artifact Awards
🏆 Distinguished Reviewer Awards
⭐ Noteworthy Reviewer Recognition
🥷 Ninja Reviewer Recognition
secartifacts.github.io/usenixsec202...

And thank you all for your help!

CC @pvadrevu.bsky.social Tiago Heinrich