Announcing a new Azure Sentinel GitHub Leaderboard!

by Scott Muniz | Aug 12, 2020 | Azure, Microsoft, Technology, Uncategorized

This article is contributed. See the original author and article here.

Authored by Grace Hsu

Calling all security enthusiasts! As threats evolve rapidly, the only way to stay ahead of adversaries is to join forces. Working together is critical to solve the common challenges we face. The Azure Sentinel Threat Hunters community provides opportunities for you to collaborate with other security experts, troubleshoot problems, share analytics and code, and continuously learn more about new innovations in the security space. To recognize the growing list of amazing contributors to our threat hunters community, we are excited to announce a new Azure Sentinel Github Leaderboard Program!

The Azure Sentinel Github repo is a one-stop shop for you to find, use and contribute to Azure Sentinel, including analytics rules and detections, exploration queries, workbooks, playbooks, and much more. The new leaderboard program enables us to showcase the Top 20 Threat Hunters, based on their previous GitHub contributions as well as the new value they add to Azure Sentinel!

Making any contribution in the categories listed on the Github wiki and submitting Github Issues will count towards the leaderboard. In addition to earning points to level up on the leaderboard, Threat Hunters will have the opportunity to redeem exclusive badges.

As you earn points and badges, you will unlock new Threat Hunter capabilities! Stay tuned for opportunities to learn and grow. To explore existing resources, check out the Tech Community Webinars and Security Ninja Trainings!

Learn more about the Azure Sentinel Leaderboard Program, view top contributors, and start making your way up the leaderboard by contributing to the GitHub repo!

Building Image Classifiers made easy with Azure Custom Vision

by Scott Muniz | Aug 12, 2020 | Azure, Microsoft, Technology, Uncategorized

This article is contributed. See the original author and article here.

By Jaya Mathew, Richin Jain and Pritish Devurkar

In our previous blog, we outlined that Supervised Machine Learning (ML) models need labeled data, but majority of the data collected in the raw format lacks labels. So, the first step before building a ML model would be to get the raw data labeled by domain experts. To do so, we had outlined how Doccano is an easy tool for collaborative text annotation. However, not all data that gets collected is in text format, many a times we end up with a bunch of images but the end goal is again to build a Supervised ML model. Like stated previously, the first step would be to tag these images with specific labels. Image tagging as well as building and even deploying either a multi-class or a multi-label classifier can be done in a few simple steps using Azure Custom Vision. 

What is Azure Custom Vision? 
 

Azure Custom Vision is a cognitive service that enables the user to specify the labels for the images, build, deploy, and improve your image classifiers. The tool enables the user to easily label the images at the time of upload. Then the algorithm trains using these images and calculates the model performance metrics. The Custom Vision service is optimized to quickly recognize major differences between images, so the user can start prototyping the model with a small amount of data (50 images per label is generally considered a good start). Once the algorithm is trained, the user can test, retrain, and eventually use the newly trained model to classify new images according to the needs of their end application. The user also can also export the trained model for offline use. 

Getting started:

To get started, the user would need to first create an Azure account and then create a new project as shown below. During the initial setup, the user is to name their project, select a resource group and determine whether the project is a Classification/Object detection scenario. In this sample, we will get started with a ‘Classification’ project which is of type ‘Multiclass (single tag per image)’. Since our images are generic, we pick the Domains as being ‘General’.

Once the project is created, the user can define the tags upfront or as they upload the images. Images can be uploaded using the ‘Add Images’ option, which prompts the user to navigate to the location of the image and enables the user to tag and upload the image. Bulk import of images is also an option. You can also use the smart-labeled option after the model has been trained.

Continue to upload all the images available for classification and then tag all the images as shown below:

Once the user has successfully uploaded and tagged their images, the next step would be to train the classifier. The user can opt for either a quick/advanced training based on cost as well as time constraints. The quality of the classifier depends on the amount, quality, and variety of the labeled data provided as well as how balanced the overall dataset is. 

After the training is complete, review the training results. The user can benchmark their model using the quick training option and then use more advanced training if the results are not satisfactory. 

If the model performance is not satisfactory, the user can also add some more images per class and then retrain the model. 

Once the model performance is deemed to be satisfactory, the user can test some more images using this option:

Now that you have built a classifier model, the user can use the Custom Vision service for image classification in production ready systems. To do so, the user would need to first publish the model which creates an endpoint for use in production ready systems. Overall based on our experience, Azure Custom Vision reduces the complexity involved in building and deploying a Custom Vision model.

References:

• https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home
• https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier
• https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/test-your-model

24 Hours of ‘Technical Geekery’ at Virtual Azure Community Day

by Scott Muniz | Aug 12, 2020 | Azure, Microsoft, Technology, Uncategorized

This article is contributed. See the original author and article here.

It is harder than ever to bring multiple people from multiple countries together in one place right now.

However, thanks to some “technical geekery” and organizing superpowers, this is exactly what took place when Azure lovers from around the world gathered online for 24 hours at the end of July.

The Virtual Azure Community Day is a multi-track, multi-cultural, and multi-discipline marathon which reunites Azure enthusiasts in the virtual world. The July 28 event produced almost 80 hours of content in six languages on the Azure Day platform. There, users could seamlessly switch between four tracks and interact in live chats. Moreover, 14 sessions were streamed live to Learn TV, 12 streams received interpretation into International Sign, and all streams were reproduced on YouTube and Twitch.

The effort to be as inclusive as possible paid off with the content reach exceeding more than 4 million. But beyond the numbers, the organizing team of Virtual Azure Community Day was most proud of the diverse line-up of speakers and emcees who shared their passion for Azure.

For example, African MVPs Oluwawumiju Oluwaseyi and Ayodeji Folarin say the event enabled them to present on their Azure experiences and learn from other international use cases. “We were very excited to work with fellow MVPs and Microsoft across the world because it exposes us to divergence in the knowledge and the different scenarios of Azure,” they say. “This keeps us on our toes to improve and optimize our current usage.”

International representation was also an important element to the Chinese representatives. Chinese Microsoft Azure and AI MVP Hao Hu says events like Virtual Azure Community Day help the nation’s Microsoft community overcome any social media obstacles to be seen and heard. “Global events like this or Azure Bootcamp and AI Bootcamp are a great chance to let global communities see us,” he says.

Meanwhile, this year was supposed to mark the debut international speeches of Nepalese Azure MVP Ravi Mandal. While the coronavirus impeded those plans, Ravi says he was excited to take his technical talks online at the marathon. “I immediately realized that this was for me because this would be the first talk I would give for an audience outside Nepal. Though online, it marks my first talk for international audiences!” 

Similarly, multiple MVPs relished the chance to connect with other Azure fans regardless of location or language. “I love to work with these people, it’s always nice to rely on someone when you get stuck,” says Danish AI MVP Eva Pardi. Likewise, Portuguese OAS MVP Nuno Arias Silva says, “[At Microsoft] we all have different experiences and sharing is the most beautiful thing that we can do.” 

“The world is smaller and smaller, and the walls between us are getting thinner and thinner,”  Romanian Azure MVP Radu Vunvulea says. “The experience that I had with people from the Microsoft ecosystem was amazing.” This point is reinforced by Netherlands Azure MVP Sjoukje Zaal, who believes there was a major benefit to creating this event as one which was not bound by timezone restrictions.

“With the current state of affairs, I think having an easy way to share all of this content is the best support we could ask for,” says South African Windows Development MVP Gergana Young. This point is driven home by Japanese Microsoft Azure MVP Yuto Takei, who says: “It’s really amazing to see presentations in different languages taking place at the same time and lasting 24 hours!”

“Honestly, since I began my career I have been seeking for likeminded people – newbies, experts, gurus – so that I can exchange ideas, learn, practice, or simply discuss!” says Bulgarian AI MVP Ekaterina Marinova. “The MVPs, Microsoft employees, everybody within this mini world has the spirit and it is contagious.” Canadian Dev MVP Hamida Rebai agrees the event successfully integrated Azure users of all experience levels to enable the sharing of knowledge. 

“For more than 10 years I have been part of different communities inside Microsoft,” says Argentine Ivana Tilca. “First I was a Microsoft student partner, then I worked at Microsoft, and now I have the opportunity to be an MVP – and it always felt like being at home,” she says.

The Virtual Azure Community Day organizing team – comprised of Henk Boelman, Suzanne Daniels, Nick Trogh, Olivier Leplus, Floor Drees, Lucie Simeckova, and Maud Levy – thank all who contributed on the day to make it possible, with special thanks to AI MVP Willem Meints.

Check out all the action from the event at the YouTube channel.