What’s new in Cloudflare: Account Owned Tokens and Zaraz Automated Actions

In October 2024, we started publishing roundup blog posts to share the latest features and updates from our teams. Today, we are announcing general availability for Account Owned Tokens, which allow organizations to improve access control for their Cloudflare services. Additionally, we are launching Zaraz Automated Actions, which is a new feature designed to streamline event tracking and tool integration when setting up third-party tools. By automating common actions like pageviews, custom events, and e-commerce tracking, it removes the need for manual configurations.

Improving access control for Cloudflare services with Account Owned Tokens

Cloudflare is critical infrastructure for the Internet, and we understand that many of the organizations that build on Cloudflare rely on apps and integrations outside the platform to make their lives easier. In order to allow access to Cloudflare resources, these apps and integrations interact with Cloudflare via our API, enabled by access tokens and API keys. Today, the API Access Tokens and API keys on the Cloudflare platform are owned by individual users, which can lead to some difficulty representing services, and adds an additional dependency on managing users alongside token permissions.

What’s new about Account Owned Tokens

First, a little explanation because the terms can Continue reading

AI for Network Engineers: Convolutional Neural Network

 Introduction


The previous chapter explained how Feed-forward Neural Networks (FNNs) can be used for multi-class classification of 28 x 28 pixel handwritten digits from the MNIST dataset. While FNNs work well for this type of task, they have significant limitations when dealing with larger, high-resolution color images.

In neural network terminology, each RGB value of an image is treated as an input feature. For instance, a high-resolution 600 dpi RGB color image with dimensions 3.937 x 3.937 inches contains approximately 5.58 million pixels, resulting in roughly 17 million RGB values.

If we use a fully connected FNN for training, all these 17 million input values are fed into every neuron in the first hidden layer. Each neuron must compute a weighted sum based on these 17 million inputs. The memory required for storing the weights depends on the numerical precision format used. For example, using the 16-bit floating-point (FP16) format, each weight requires 2 bytes. Thus, the memory requirement per neuron would be approximately 32 MB. If the first hidden layer has 10,000 neurons, the total memory required for storing the weights in this layer would be around 316 GB.

In contrast, Convolutional Neural Networks (CNNs) use Continue reading

D2DO257: Love is in the Firmware

With the current cultural emphasis on AI and how that is changing our world, we often forget the human element of individuals and teams when building an effective software development team. On today’s Day Two DevOps, we explore the psychology behind software teams, psychological safety for those teams and how the advent of AI plays... Read more »

PP039: Securing Active Directory from a Pen Tester’s Perspective

Microsoft’s Active Directory and Entra ID are valuable targets for attackers because they store critical identity information. On today’s Packet Protector, we talk with penetration tester and security consultant Eric Kuehn about how he approaches compromising AD/Entra ID, common problems he sees during client engagements, quick wins for administrators and security pros to fortify their... Read more »

Worldwide deployment of real-time flow analytics

Industry standard sFlow telemetry is widely supported by network equipment vendors and network management platforms. However, the advent of real-time sFlow analytics has opened up a range of new applications for sFlow. The map above shows the proportion of sFlow-RT instances running in each of the over 70 countries in which it is deployed.

The following use cases are driving current deployments:

Addressing the challenge of operating AI / ML clusters is the emerging application for sFlow visibility. High speed (400/800G) data center switches needed to handle machine learning traffic flows include sFlow agents and real-time analytics are essential to optimize the network so that expensive GPU and compute resources are fully utilized, see Leveraging open technologies to monitor packet drops in AI cluster fabrics.

If you would like to see how real-time network analytics can transform network operations, Getting Started describes how to download and configure sFlow-RT analytics software for use in your network, or how to try it out using an emulator, or pre-captured data.

NB503: Apple Buys Stake in Satellite Telecom Company; Arista Enjoys AI Revenue Bump

Take a Network Break! This week we cover a serious Cisco vulnerability, SonicWall offering a firewall warranty, and a security advisory from ID provider Okta. Apple buys a stake in a satellite telecommunications company, Lumen and Google team up on a joint networking/AI deal, and we check on financial results from Arista Networks, Extreme Networks,... Read more »

Custom Arm CPUs Drive Many Different AI Approaches In The Datacenter

Sponsored Feature  Arm is starting to fulfill its promise of transforming the nature of compute in the datacenter, and it is getting some big help from traditional chip makers as well as the hyperscalers and cloud builders that have massive computing requirements and who also need to drive efficiency up and costs down each and every year. …

Custom Arm CPUs Drive Many Different AI Approaches In The Datacenter was written by Timothy Prickett Morgan at The Next Platform.

Customize Caddy’s plugins with Nix

Caddy is an open-source web server written in Go. It handles TLS certificates automatically and comes with a simple configuration syntax. Users can extend its functionality through plugins1 to add features like rate limiting, caching, and Docker integration.

While Caddy is available in Nixpkgs, adding extra plugins is not simple.2 The compilation process needs Internet access, which Nix denies during build to ensure reproducibility. When trying to build the following derivation using xcaddy, a tool for building Caddy with plugins, it fails with this error: dial tcp: lookup proxy.golang.org on [::1]:53: connection refused.

{ pkgs }:
pkgs.stdenv.mkDerivation {
  name = "caddy-with-xcaddy";
  nativeBuildInputs = with pkgs; [ go xcaddy cacert ];
  unpackPhase = "true";
  buildPhase =
    ''
      xcaddy build --with github.com/caddy-dns/[email protected]
    '';
  installPhase = ''
    mkdir -p $out/bin
    cp caddy $out/bin
  '';
}

Fixed-output derivations are an exception to this rule and get network access during build. They need to specify their output hash. For example, the fetchurl function produces a fixed-output derivation:

{ stdenv, fetchurl }:
stdenv.mkDerivation rec {
  pname = "hello";
  version = "2.12.1";
  src  Continue reading

How We Measure: ISP User Counts

At APNIC Labs we generate, on a daily ongoing basis, our estimate of the number of users per ISP for every ISP that we see on the Internet through the ad-based measurement platform. This report is published at the URL: https://stats.labs.apnic.net/aspop. As far as we are aware this is the only such public data set that encompasses the entirety of the public Internet. Here I would like to explain how we calculate this data, and provide some responses to a recent presentation at the RIPE 89 meeting on this data set.

From Python To Go 002. Basic Data Types (Numeric, Boolean, String) and Variables.

Hello my friend,

We continue our blog series about learning Go (Golang) as second programming language, which you can use for network and IT infrastructure automation. Today we’ll talk about the basic data types and variables both in Python and Go

How To Start Automating?

Any programming language, whether it is Python or Go (Golang), is a tool to implement your business logic. Whilst it is very important to be experienced with the tool, it is important also to understand the wide context of network automation, and this is where our trainings will kick start you:

We offer the following training programs in network automation for you:

During these trainings you will learn the following topics:

  • Success and failure strategies to build the automation tools.
  • Principles of software developments and the most useful and convenient tools.
  • Data encoding (free-text, XML, JSON, YAML, Protobuf).
  • Model-driven network automation with YANG, NETCONF, RESTCONF, GNMI.
  • Full configuration templating with Jinja2 based on the source of truth (NetBox).
  • Best programming languages (Python, Bash) for developing automation
  • The most rock-solid and Continue reading

Weekend Reads 110924


Toward the end of August 2024, a customized malware dubbed “Voldemort” based on strings found in its code was used in a cyber espionage campaign targeting various countries.


Until 2019, it was a Google partner running full Android. Then it forked its own version of Android, called it HarmonyOS, stopped including Google proprietary apps, and set up its own Android app store.


As previously discussed in this post, Xiong’an New Area (Xiong’an), a pilot city established in 2017 about 100 kilometres west of Beijing, aims to be a model for future digital cities, built with IPv6-only infrastructure from the start.


A little over three dozen security vulnerabilities have been disclosed in various open-source artificial intelligence (AI) and machine learning (ML) models, some of which could lead to remote code execution and information theft.


Today, we’re excited to announce the launch of Kentik’s Cloud Latency Map, a public service that uses Kentik Synthetics to continuously measure latency between the regions of the biggest cloud providers.


Data can undergo a similar transformation—two pieces of data, linked, such that changing one has an impact on the other. Think of this as “spooky data at a distance.”


In this work, we aim to Continue reading

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