ebursztein
Autonomous Timeline Analysis and Threat Hunting Black Hat Talk Slides
Google announces Sec-Gemini v1 a new experimental cybersecurity model
Show HN: Local RAG with Ollama, Gemma and RETSim
Lessons Learned from Developing Secure AI Workflows at Google
For crawling we have planned a head only model to avoid fetching the whole file but it is not ready yet -- we weren't sure what use-cases would emerge so that is good to know that such model might be useful.
We mostly use Magika internally to route files for AV scanning as we wrote in the blog post, so it is possible that despite our best effort to test Magika extensively on various file types it is not as good on fonts format as it should be. We will look into.
Thanks again for sharing your experience with Magika this is very useful.
Overall file takes about 6ms (single file) 2.26ms per files when scanning multiples. Magika is at 65ms single file and 5.3ms when scanning multiples.
So Magika is for the worst case scenario about 10x slower due to the time it takes to load the model and 2x slower on repeated detection. This is why we said it is not that much slower.
We will have more performance measurements in the upcoming research paper. Hope that answer the question
The release include the python package and the cli which are quite fast and is the main way we did expect people to use -- sorry if that hasn't be clear in the post.
The goal of the release is to offer a tool that is far more accurate that other tools and works on the major file types as we hope it to be useful to the community.
Glad to hear it worked on your files
Show HN: Magika: AI powered fast and efficient file type identification
We are very excited to announce the release of Magika our AI powered fast and efficient file type identification lib and tool - https://opensource.googleblog.com/2024/02/magika-ai-powered-fast-and-efficient-file-type-identification.htmlThanks to its optimized Keras model, large scale training dataset, and Onnx Magika massively outperform other file identification tools while be very fast even...
Show HN: RETVec: Resilient and Efficient Text Vectorizer
Happy Friday,Really happy to share that the code and model for RETVec our new SOTA robust text tokenizer for classification is available on Github: https://github.com/google-research/retvec/ and the NeurIPS paper on Arxiv: https://arxiv.org/abs/2302.09207Beside its compactness and robustness one of the RETVec strong point is that it greatly simplify the creation of on-device models: RETVec work...
Making security keys quantum resilient
On the consequences of AI workforce entering the market
Show HN: Etteilla Foundation my side project
Hi,After over two years of intense efforts, I am thrilled to share that the Etteilla Foundation and its website https://etteilla.org are finally live. The Etteilla Foundation is a 501(c)(3) non-profit organization dedicated to preserving and promoting the rich cultural heritage of playing cards.Over the last two years, as side project, I created the foundation and spend my weekend and vacations...
AI helps keeping Gmail inboxes malware free [slides]
This talk recounts how in the last few years we researched and developed a specialized office document scanner that combines a custom document analyzer with deep-learning to detect malicious docx and xls that bypass standard AVs. In 2021 our AI scanner was able to detect 36% additional malicious documents that eluded other scanners on average and 178% at peak performance.
I hope you will find those slides useful and informative. If you have any questions, please ask away, will do my best to answer :)