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Stop Modern Security Attacks in Real Time with ARIA Cybersecurity and NVIDIA

The newly announced ARIA Zero Trust Gateway uses NVIDIA BlueField DPUs to provide a sophisticated cybersecurity solution.

Today’s cybersecurity landscape is changing in waves with threat and attack methods putting the business world on high alert. Modern attacks continue to gain sophistication, staying one step ahead of traditional cyber defense measures, by continuously altering attack techniques. With the increasing use of AI, ML, 5G, and IoT, network speeds readily run at 100G rates or more. Current methods of detection for dangerous threats and attacks are quickly becoming outdated and ineffective. 

When operating at modern network speeds, it is no longer possible for security teams to monitor an entire network in real time. Organizations have tried to augment their Intrusion Prevention Systems (IPS) by relying on employees to perform the deep analysis of suspicious activity. This requires highly trained and paid security operations center personnel and comes with significant risks when teams are unable to keep pace with the deluge of data.

The litany of security issues has forced organizations to seek a next-generation solution that provides the ability to analyze all traffic within their 100G networks. To accomplish this requires a next generation IPS that is automated, more effective, and efficient that can stop attacks in real time at 100G and beyond.  

ARIA Zero Trust Security Gateway on BlueField-2 Data Processing Unit 

ARIA Cybersecurity has developed a solution to solve these problems called the ARIA Zero Trust (AZT) Gateway. The gateway uses hardware acceleration from the NVIDIA BlueField-2 Data Processing Unit (DPU) for line-rate analysis. The solution is deployed as a compact, in-line, standalone network device to stop attacks with no impact to production traffic crossing the wire.  

To accomplish this, the gateway operates by analyzing each packet in real time. Creating analytics for threat analysis enforces not only existing standalone protection policies, but also new dynamically generated policies, to ensure a more effective security posture.

To detect attacks, the packet analytics go to the central brain of the solution. The brain is ARIA’s Advanced Detection & Response product that uses ML and AI-driven threat models to analyze data for over 70 different types of attacks using the MITRE ATT&CK framework. 

This is accomplished in a totally automated approach, removing the need for manual human involvement. In addition, copies of specific traffic conversations can be sent to an Intrusion Detection System for deeper analysis.  

In both cases, the results are relayed back to the AZT Gateway as dynamically generated rules to eliminate the identified attack conversations in real time. Attacks can be identified, logged as new rules, and stopped in just seconds, automatically. Performing this process in real time is a major breakthrough.

Previously, you may have only been alerted to a problem and left to figure out the solution on your own. However, with the AZT Gateway, you can now take automated action on alerts and deploy new security rules to prevent unwanted activities on your network.

A diagram showing the network traffic flowing through the BlueField DPU and AZT Gateway to end user devices
Figure 1. ARIA Zero Trust Security Gateway protocol diagram

Real-time security analysis

The BlueField-2 DPU is available in a compact PCIe OCP Card form factor. It provides the ability to monitor two 100G links at a time, without dropping any traffic or consuming critical CPU resources. 

BlueField also provides hardware acceleration to handle packet analysis at demanding networking speeds in excess of 100G. To classify each packet at a conversation level, the AZT system software leverages the highly efficient BlueField Arm cores to apply preset and dynamically generated security rulesets. 

The compact footprint and low-power draw make BlueField ideal for deployment in network provider and Cloud data center environments. Using BlueField, the AZT Gateway can also look into encrypted data stream payloads for deeper analysis. 

This solution has proven to lower the cost per protected packet by up to 10X, decrease power compared to alternatives, and reduce the rack footprint by 10X.

For upcoming enhancements, the NVIDIA Morpheus application framework will provide ARIA with evolving cybersecurity analysis using advanced ML and AI algorithms to find and stop sophisticated attacks in real time.

Watch the ARIA Zero Trust Security Gateway webinar. >>

Learn how to accelerate your network with the BlueField DPU. >>

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Misc

Upcoming Event: How OneCup AI Created Betsy, the AI Ranch Hand

On June 21, learn how OneCup AI leveraged the NVIDIA TAO toolkit and DeepStream SDK to create automated vision AI that helps ranchers track and monitor livestock.

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Festo Develops With Isaac Sim to Drive Its Industrial Automation

Dionysios Satikidis was playing FIFA 19 when he realized the simulated soccer game’s realism offered a glimpse into the future for training robots. An expert in AI and autonomous systems at Festo, a German industrial control and automation company, he believed the worlds of gaming and robotics would intersect. “I’ve always been passionate about technology Read article >

The post Festo Develops With Isaac Sim to Drive Its Industrial Automation appeared first on NVIDIA Blog.

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What Is Zero Trust?

For all its sophistication, the Internet age has brought on a digital plague of security breaches. The steady drumbeat of data and identity thefts spawned a new movement and a modern mantra that’s even been the subject of a U.S. presidential mandate — zero trust. So, What Is Zero Trust? Zero trust is a cybersecurity Read article >

The post What Is Zero Trust? appeared first on NVIDIA Blog.

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Feel the Need … for Speed as ‘Top Goose’ Debuts In the NVIDIA Studio

This week In the NVIDIA Studio takes off with the debut of Top Goose, a short animation created with Omniverse Machinima and inspired by one of the greatest fictional pilots to ever grace the big screen. The project was powered by PCs using the same breed of GPU that has produced every Best Visual Effects nominee at the Academy Awards for 14 years: multiple systems with NVIDIA RTX A6000 GPUs and an NVIDIA Studio laptop — the Razer Blade 15 with a GeForce RTX 3070 Laptop GPU.

The post Feel the Need … for Speed as ‘Top Goose’ Debuts In the NVIDIA Studio appeared first on NVIDIA Blog.

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Looking for help for hire

I’m both a collector and a coin dealer. I look through tens of thousands of coins a week for rare dates, errors, etc. But as I get older, my eyes are not what they use to be. So it’s getting somewhat difficult for me to see the key details on the coin. So I decided to make a setup that can look through coins for me. I’ve been greatly influenced by this machine that does everything I want, but I need something a lot smaller.

https://youtu.be/k7okDtRRCcY

I do have a basic background in coding and how it works. But I have little experience with making an AI. I’ve watched many video tutorials and I now understand clearly how an AI learns. I think the best route is to use Python, TensorFlow, and open-cv. But I keep getting some kind of errors that have been a major roadblock for me.

If this is relevant. My company setup is a ryzen 9 5900x. 3080 gpu and has 64gb of ram.

I’m looking for someone who can guide me through installing and training an AI model. I will compensate for your time, either in money or in collectible coins. What I mean for collectable coins is good quality coins. Not those cheapy coins you pick up from gift shops. But actually pieces of history. I’ve got silver coins, I’ve got a ton of English coins from 1600s-1800s. You can check out my ebay store to get a idea of what I have to offer. https://www.ebay.com/sch/uncommoncentscoins/m.html?_nkw&_armrs=1&_ipg&_from&LH_Complete=1&LH_Sold=1&rt=nc&_trksid=p2046732.m1684

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Where to start with AI image generation?

Hey guys,

I was chatting with an artist today and he was showing me some AI art he created. Basically he’d create base artwork and then process it through an AI to add some random stylization. I asked him about the process and he was pretty secretive about it, but mentioned he uses Tensor Flow. He couldn’t give any more details.

I’m in love with the idea and I was curious if anyone knew of any sample projects that do something similar, or any resources to get me started?

My background: software dev, but not much in AI

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Colab gives runtime error, failed to initialize sdl.

Hi,

I was using google colab when I was struck with this issue. I have all the necessary libs installed. This is the error message:

RuntimeError Traceback (most recent call last)
<ipython-input-13-feca46536a5c> in <module>() —-> 1 env = gym.make(‘ALE/Breakout-v5′, render_mode=’human’) 2 env = Recorder(env, ‘./video’) 4 frames
/usr/local/lib/python3.7/dist-packages/gym/envs/atari/environment.py in seed(self, seed) 194 “
https://github.com/mgbellemare/Arcade-Learning-Environment#rom-management” 195 ) –> 196 self.ale.loadROM(getattr(roms, self._game)) 197 198 if self._game_mode is not None: RuntimeError: Failed to initialize SDL

Cudn’t find any solutions, pls help.

Thx

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Simplify AI Model Development with the Latest TAO Toolkit Release

Boost productivity and model training with new pretrained models and features such as ONNX model weights import, REST APIs, and TensorBoard visualization.

Today, NVIDIA announced the general availability of the latest version of the TAO Toolkit. As a low-code version of the NVIDIA Train, Adapt and Optimize (TAO) framework, the toolkit simplifies and accelerates the creation of AI models for speech and vision AI applications. 

With TAO, developers can use the power of transfer learning to create production-ready models customized and optimized for many use-cases. These include detecting defects, translating languages, or managing traffic—without the need for massive amounts of data. 

This version boosts developer productivity with new pretrained vision and speech models. It also includes key new features such as ONNX model weights import, REST APIs, and TensorBoard integration. 

Download TAO Toolkit 3.22.05 >>

Release highlights

Deploy TAO Toolkit as-a-Service with REST APIs: Build a new AI service or integrate into an existing one with REST APIs. You can manage and orchestrate the TAO Toolkit service on Kubernetes. With TAO Toolkit as-a-service IT managers can deliver scalable services using industry-standard APIs.

Bring your own model weights: Fine-tune and optimize your non-TAO models with TAO. Import pretrained weights from ONNX and take advantage of TAO features like pruning and quantization on your own model. This is supported for image classification and segmentation tasks.

Visualize with TensorBoard: Understand your model training performance by visualizing scalars such as training and validation loss, model weights, and predicted images in TensorBoard. Compare results between experiments by changing hyperparameters and choose the one that best fits your needs. 

Pretrained models: Pretrained models speed up the customization process for you to fine-tune through the power of transfer learning, with less data. 

Some of the new pretrained models in this latest version can: 

  • Apply data gathered from LIDAR sensors for robotics and automotive applications.
  • Classify human actions based on human poses that can be used in public safety, retail, and worker safety use cases.
  • Estimate keypoints on humans, animals, and objects to help portray actions or simply define the object shape.  
  • Create custom voices with just 30 minutes of recorded data to power smart devices, game characters, and quick service restaurants.

Enterprise support for TAO Toolkit is available with NVIDIA AI Enterprise, an end-to-end software suite for AI development and deployment. This new release of TAO Toolkit  will be included in the next quarterly update to NVIDIA AI Enterprise.

Get started 

Solutions using TAO Toolkit 

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Upcoming Event: Join NVIDIA at Automate 2022

Join NVIDIA at Automate 2022, June 6-9, to learn about AI platforms for manufacturing, robotics, and logistics that improve efficiency, scalability, and production across industries.