COMMISSIONED Enterprises adopting AI to stay competitive must tailor AI models to their needs. …
Mastering The AI Terrain: Why Optimal Storage Is Essential For Competitive Edge was written by Timothy Prickett Morgan at The Next Platform.
Historically, traffic graphs on Cloudflare Radar have displayed two metrics: total traffic and HTTP traffic. These graphs show normalized traffic volumes measured in bytes, derived from aggregated NetFlow data. (NetFlow is a protocol used to collect metadata about IP traffic flows traversing network devices.) Today, we’re adding an additional metric that reflects the number of HTTP requests, normalized over the same time period. By comparing bytes with requests, readers can gain additional insights into traffic patterns and user behavior. Below, we review how this new data has been incorporated into Radar, and explore HTTP request traffic in more detail.
Note that while we refer to “HTTP request traffic” in this post and on Radar, the term encompasses requests made in the clear over HTTP and over encrypted connections using HTTPS – the latter accounts for ~95% of all requests to Cloudflare during July 2024.
Graphs including HTTP request-based traffic data have been added to the Overview and Traffic sections on Cloudflare Radar. On the Overview page, the “Traffic trends” graph now includes a drop-down selector at the upper right, where you can choose between “Total & HTTP bytes” and “HTTP requests & bytes”. We explore Continue reading
Historically, traffic graphs on Cloudflare Radar have displayed two metrics: total traffic and HTTP traffic. These graphs show normalized traffic volumes measured in bytes, derived from aggregated NetFlow data. (NetFlow is a protocol used to collect metadata about IP traffic flows traversing network devices.) Today, we’re adding another metric that reflects the number of HTTP requests, normalized over the same time period. By comparing bytes with requests, readers can gain additional insights into traffic patterns and user behavior. Below, we review how this new data has been incorporated into Radar, and explore HTTP request traffic in more detail.
Note that while we refer to “HTTP request traffic” in this post and on Radar, the term encompasses requests made in the clear over HTTP and over encrypted connections using HTTPS – the latter accounts for ~95% of all requests to Cloudflare during July 2024.
Graphs including HTTP request-based traffic data have been added to the Overview and Traffic sections on Cloudflare Radar. On the Overview page, the “Traffic trends” graph now includes a drop-down selector at the upper right, where you can choose between “Total & HTTP bytes” and “HTTP requests & bytes”. We explore Continue reading
In the previous blog post, I described how to build a lab to explore the layer-3-only EVPN design and asked you to do that and figure out what’s going on behind the scenes. If you didn’t find time for that, let’s do it together in this blog post. To keep it reasonably short, we’ll focus on the EVPN control plane and leave the exploration of the data-plane data structures for another blog post.
The most important thing to understand when analyzing a layer-3-only EVPN/VXLAN network is that the data plane looks like a VRF-lite design: each VRF uses a hidden VLAN (implemented with VXLAN) as the transport VLAN between the PE devices.
We’re thrilled to announce Cloudflare’s worldwide 2023 Channel Partner Award winners! Partners are crucial to Cloudflare’s success, extending the solutions and support that customers need to control application complexity, reduce cyber risk, and cut costs, all with a high level of customer satisfaction.
This year, we again received CRN’s highest accolade of a 5-star ranking for our Partner Program. Through our expanded Cloudflare PowerUP Partner Program, we’re ensuring Cloudflare’s partnerships and alliances continue delivering strong results to joint customers across sectors worldwide. We’re focused on making it easier for our partners to work with us and grow their business with us. The Cloudflare team is all about helping partners:
Be innovative by transforming how customers connect, protect, and build with Cloudflare security, speed, programmability, and resilience.
Increase profitability by growing revenue and delivering more value at scale to rapidly grow business and expand reach.
Accelerate GTM by benefiting from sales and marketing support, streamlined processes, and transparent pricing to close deals quickly.
From comprehensive training through Cloudflare University to expert support across departments, partners are equipped to drive digital transformation and modernize IT infrastructures for their customers in a competitive market.
This post is also available in Deutsch, Español, 简体中文, 繁體中文, 日本語 and 한국어.
We’re thrilled to announce Cloudflare’s worldwide 2023 Channel Partner Award winners! Partners are crucial to Cloudflare’s success, extending the solutions and support that customers need to control application complexity, reduce cyber risk, and cut costs, all with a high level of customer satisfaction.
This year, we again received CRN’s highest accolade of a 5-star ranking for our Partner Program. Through our expanded Cloudflare PowerUP Partner Program, we’re ensuring Cloudflare’s partnerships and alliances continue delivering strong results to joint customers across sectors worldwide. We’re focused on making it easier for our partners to work with us and grow their business with us. The Cloudflare team is all about helping partners:
From comprehensive training through Cloudflare University to expert support across departments, partners are equipped to drive digital Continue reading
Here’s a question for you: How much of the growth in cloud spending at Microsoft Azure, Amazon Web Services, and Google Cloud in the second quarter came from OpenAI and Anthropic spending money they got as investments out of the treasure chests of Microsoft, Amazon, and Google? …
The Sugar Daddy Boomerang Effect: How AI Investments Puff Up The Clouds was written by Timothy Prickett Morgan at The Next Platform.
For many years, Cloudflare has used advanced fingerprinting techniques to help block online threats, in products like our DDoS engine, our WAF, and Bot Management. For the purposes of Bot Management, fingerprinting characteristic elements of client software help us quickly identify what kind of software is making an HTTP request. It’s an efficient and accurate way to differentiate a browser from a Python script, while preserving user privacy. These fingerprints are used on their own for simple rules, and they underpin complex machine learning models as well.
Making sure our fingerprints keep pace with the pace of change on the Internet is a constant and critical task. Bots will always adapt to try and look more browser-like. Less frequently, browsers will introduce major changes to their behavior and affect the entire Internet landscape. Last year, Google did exactly that, making older TLS fingerprints almost useless for identifying the latest version of Chrome.
JA3 fingerprint introduced by Salesforce researchers in 2017 and later adopted by Cloudflare, involves creating a hash of the TLS ClientHello message. This hash includes the ordered list of TLS cipher suites, extensions, and other parameters, providing a unique identifier for each Continue reading
For many years, Cloudflare has used advanced fingerprinting techniques to help block online threats, in products like our DDoS engine, our WAF, and Bot Management. For the purposes of Bot Management, fingerprinting characteristic elements of client software help us quickly identify what kind of software is making an HTTP request. It’s an efficient and accurate way to differentiate a browser from a Python script, while preserving user privacy. These fingerprints are used on their own for simple rules, and they underpin complex machine learning models as well.
Making sure our fingerprints keep pace with the pace of change on the Internet is a constant and critical task. Bots will always adapt to try and look more browser-like. Less frequently, browsers will introduce major changes to their behavior and affect the entire Internet landscape. Last year, Google did exactly that, making older TLS fingerprints almost useless for identifying the latest version of Chrome.
These methods are instrumental in accurately scoring and classifying bots, enhancing security measures, and enriching data analytics capabilities. Below are some examples of the fingerprinting techniques we have implemented over the years:
HTTP Signature: The HTTP Signature technique involves analyzing HTTP Continue reading
Wes made an interesting comment to the Migrating a Data Center Fabric to VXLAN blog post:
The benefit of VXLAN is mostly scalability, so if your enterprise network is not scaling… just don’t. The migration path from VLANs is to just keep using VLANs. The (vendor-driven) networking industry has a huge blind spot about this.
Paraphrasing the famous Dinesh Dutt’s Autocon1 remark: I couldn’t disagree with you more.
Recently, walking has become a habit for me, and I absolutely love taking short walks while listening to music or podcasts. It helps me rest and recover mentally, emotionally, and physically. Walking clears my mind and helps me process my emotions and thoughts. In this post, I will cover the benefits I experience from walking and how it improves my productivity.
Every time I feel stressed or need a break from everything around me, I go for a walk. It's a simple act, but it refreshes me more than anything else. These quiet moments alone with my thoughts are important for recharging and gaining a new perspective on whatever is bothering me. Walking not only gives me the physical exercise I need but also eases my mind and allows me to return to my tasks with renewed energy and focus.
Feed-forward Neural Networks are suitable for simple tasks like basic time series prediction without long-term relationships. However, FNNs is not a one-size-fits-all solution. For instance, digital image training process uses pixel values of image as input data. Consider training a model to recognize a high resolution (600 dpi), 3.937 x 3.937 inches digital RGB (red, green, blue) image. The number of input parameters can be calculated as follows:
Width: 3.937 in x 600 ≈ 2362 pixels
Height: 3.937 in x 600 ≈ 2362 pixels
Pixels in image: 2362 x 2362 = 5,579,044 pixels
RGB (3 channels): 5,579,044 pxls x 3 channels = 16 737 132
Total input parameters: 16 737 132
Memory consumption: ≈ 16 MB
FNNs are not ideal for digital image training. If we use FNN for training in our example, we fed 16,737,132 input parameters to the first hidden layer, each having unique weight. For image training, there might be thousands of images, handling millions of parameters demands significant computation cycles and is a memory-intensive process. Besides, FNNs treat each pixel as an independent unit. Therefore, FNN algorithm does not understand dependencies between pixels and cannot recognize the same image if it shifts within the frame. Besides, FNN does not detect edges and other crucial details.
A better model for training digital images is Convolutional Neural Networks (CNNs). Unlike in FFN neural networks where each neuron has a unique set of weights, CNNs use the same set of weights (Kernel/Filter) across different regions of the image, which reduces the number of parameters. Besides, CNN algorithm understands the pixel dependencies and can recognize patterns and objects regardless of their position in the image.
The input data processing in CNNs is hierarchical. The first layer, convolutional layers, focuses on low-level features such as textures and edges. The second layer, pooling layer, captures higher-level features like shapes and objects. These two layers significantly reduce the input data parameters before they are fed into the neurons in the first hidden layer, the fully connected layer, where each neuron has unique weights (like FNNs).
Disclaimer: All Writings And Opinions Are My Own And Are Interpreted Solely From My Understanding. Please Contact The Concerned Support Teams For A Professional Opinion, As Technology And Features Change Rapidly.
And No! This can’t replace the accuracy of static templating configurations. This helps us to better understand and develop the templates. This was almost rocket science to me when I first got to know about them.
Most modern day deployments have some sort of variable files and template files (YAML and Jinja2). These can be intimidating. It was mysterious. When I first looked at them years ago, I found them confusing. Today, with LLM you don’t have to really be worried about how to generate it. The parser in itself can come up on the fly to generate popular networking gear. More than that, it’s more than willing to take in the data to spit out whatever configuration is needed.
Lets say I just appreciated the way the configuration files are generated today. I wanted to quickly see if an LLM can generate the config. It also do the deployment for me. Then it helps me with some pre-checks, all without writing the code.
Let’s not go too far Continue reading
Hi all, welcome back to another Palo Alto Firewall blog post. In this post, we will explore how to add a VM-Series Firewall into VMWare Workstation, set up the interfaces, and make initial configurations. This guide is designed for beginners who want to try the Palo Alto Firewall in their home lab but aren’t sure where to start.
The first step is to download the image from the Palo Alto Customer Support Portal. To do this, you will need a valid support contract, as sharing the image with others is not allowed. I understand this can be frustrating, as it may seem like vendors make it difficult for users to access and use their products. However, if you use Palo Alto products at work, you might be able to download it there.
To download the required file, navigate to Updates > Software Updates in the portal. Look for the PA-VM section and download the file named PA-VM-ESX-10.1.3.ova (note that the version might differ).
When setting up the Palo Alto Firewall in VMWare Workstation, I usually allocate 8 GB of RAM, 60 GB of disk space, Continue reading