In this fascinating episode, watch how Ofir Barzilay, Principal Engineering Manager for IoT Security, demonstrates a brute force attack (https://aka.ms/iotshow/ascforiot) on a Raspberry Pi IoT device connected to Azure IoT Hub. You will see how Ofir attacks the device to discover its password.

Watch how he downloads a payload and infects the device. You will see him gain control over the device, connecting it to his command and control server to fully own it, showing how he can exploit it for crypto mining, DDOS and more.

At the end of the demo, Ofir demonstrates how Azure Security Center for IoT has monitored, detected, and reported on the entire attack. He also shows how Azure Security Center for IoT leverages Microsoft Threat Intelligence to flag suspicious devices. Solution builders using Azure IoT security will sleep better after watching this show.

Katherine Bindley of the Wall Street Journal is at CES to take a look at the latest AI-infused cameras on the market.

Two new smart systems use cameras, artificial intelligence and an assortment of sensors to keep watch over you—Patscan looks for threats in public spaces, while Eyeris monitors the driver and passengers in a car. WSJ’s Katherine Bindley visits CES to explores their advantages, as well as their privacy costs.

Join James Penney, CTO, Device Authority, (aka.ms/iotshow/deviceauthority) to learn how Device Authority’s KeyScaler platform can be used to securely onboard IoT devices to the Azure IoT Hub Device Provisioning Service in seconds without human intervention.

The KeyScaler platform solves one of the biggest challenges of IoT: onboarding of devices at scale and managing the owner-controlled identities and credentials across the different services.

Device Authority’s KeyScaler platform provides policy-driven automated PKI management for any IoT device to connect it to any Certificate Authority.

Learn all about the new data classification capabilities built into Azure SQL Database. Data Classification enables discovering, classifying, labeling & protecting the sensitive data in your databases.

Examples of sensitive data include business, financial, healthcare, personally identifiable data (PII). Discovering and classifying your most sensitive data can play a pivotal role in your organizational information protection stature.

Data discovery & classification is part of the Advanced Data Security (ADS) offering, which is a unified package for advanced SQL security capabilities.

Find out more about Advanced Data Security at: https://docs.microsoft.com/en-us/azure/sql-database/sql-database-advanced-data-security?WT.mc_id=dataexposed-c9-niner-fw .

Microsoft Research just posted this video on adversarial machine learning.

As ML is being used for increasingly security sensitive applications and is trained in increasingly unreliable data, the ability for learning algorithms to tolerate worst-case noise has become more and more important.

The reliability of machine learning systems in the presence of adversarial noise has become a major field of study in recent years.

In this talk, I’ll survey a number of recent results in this area, both theoretical and more applied. We will survey recent advances in robust statistics, data poisoning, and adversarial examples for neural networks. The overarching goal is to give provably robust algorithms for these problems, which still perform well in practice.

Talk slides: https://www.microsoft.com/en-us/research/uploads/prod/2019/11/Adversarial-Machine-Learning-SLIDES.pdf

Siraj Raval shows off examples of machine learning apps from his students.

If you’re wondering about my stance on the recent controversies around Siraj, I recorded a Data Point about that.

Machine Learning powers almost every internet service we use these days, but it’s rare to find a full pipeline example of machine learning being deployed in a web app. In this episode, I’d like to present 5 full-stack machine learning demos submitted as midterm projects from the students of my current course. The midterm assignment was to create a paid machine learning web app, and after receiving countless incredible submissions, I’ve decided to share my favorite 5 publicly. I was surprised by how many students in the course had never coded before and to see them building a full-stack web app in a few weeks was a very fulfilling experience. Use these examples as a template to help you ideate on potential business ideas to make a positive impact in the world using machine learning. And if you’d like, be sure to reach out and support each of the students I’ve demoed here today in any way can you offer. They’ve been working their butts off. Enjoy!

Presentation notebook: https://colab.research.google.com/drive/1m5aLHPnwIhVX8zgMvZUtK4iG9xSaMbk8