April 2019 - Seite 42 von 85 - Cloud Computing Köln

Designed for developers and solution architects who need to understand how to move business critical apps to the cloud, this online workshop series gets you hands-on with a proven process for migrating an existing ASP.NET based application to a container based application. Join us live for 90 minutes on Wednesday and Fridays through May 3 to get expert guidance and to get your questions answered.

The optional (but highly recommended) hands-on labs that accompany this series give you experience building a proof of concept (POC) that will deliver a multi-tiered web app solution from a Virtual Machine architecture into Azure, leveraging Azure Platform Services and different Azure container solutions available today. You will also migrate the underlying database from a SQL 2014 Virtual Machine architecture to SQL Azure.

At the end of this series you will have a good understanding of container concepts, Docker architecture and operations, Azure Container Services, Azure Kubernetes Services and SQL Azure PaaS solutioning.

Part 1: Digital App Transformation with Azure

The first session covers the strategic ways to modernize your existing .NET Framework applications. This includes the different choices Azure provides for app modernization, starting from VM lift & shift, to Platform as a Service (PAAS) as well as an overview of the container services and orchestrators Azure natively provides.

Watch on demand

Part 2: Infrastructure as Code using ARM templates

ARM (Azure Resource Manager) templates are Azure’s answer to Infrastructure as Code, and they can do much more than just deploy infrastructure resources. This session will teach you about how Infrastructure as Code enables faster execution, reduces risk, reduces costs, and integrates with DevOps. You’ll learn about why you should use ARM templates for automated deployment and continuous integration, how to find Azure Quickstart Templates on GitHub, and how to author ARM templates with Visual Studio.

Besides learning how ARM templates deploy Azure resources, we take it a step further and walk you through the full process to automate VM configuration as well. After this session you’ll be able to work through the labs we provide, where you will setup your Azure subscription and deploy the source Virtual Machine environment with Visual Studio 2017, deploying the baseline 2-tier application workload we will be using throughout the workshop series.

Watch on demand

Part 3: Azure Database Solutions | SQL Azure

We’ll start by covering SQL, IaaS, and PaaS options, including removing security and isolation concerns and how to integrate high availability / disaster recovery. You’ll see an in-depth demo of deploying Azure SQL where we will highlight key features.

Then we’ll dive deep on migration options and highlight database migration tools, so that you’ll be able to complete the accompanying lab where you migrate a SQL VM database to SQL Azure using SQL Management Studio.

April 17, 2019 10 am Pacific / 1 pm Eastern

Part 4: Azure App Services | Azure Web Apps

In this demo filled session, you’ll learn about key features, including deployment slots, scaling and autoscaling, pricing tiers, integrated backup, and app insights allowing you to understand the core capabilities and strengths of Azure Web Apps. The session concludes with Azure Web Apps for Containers, with sample architecture and deployment life cycle. In the lab for this session you’ll migrate a legacy ASP.NET application to Azure Web Apps with Visual Studio.

April 19, 2019 10 am Pacific / 1 pm Eastern

Part 5: Docker Containers

Docker Containers are the global standard and are natively supported in Azure, offering enterprises an interesting and flexible way to migrate legacy apps for both future proofing and cost benefits. In this session you’ll see detailed demos of installing Docker for Windows, running common Docker CLI operations, and how to build a Docker Image using both the CLI and Visual Studio 2017. We’ll also teach you important tips for troubleshooting Docker builds. After this session you’ll be able to complete the lab where you will containerize a legacy ASP.NET application with Docker CE for Windows.

April 24, 2019 10 am Pacific / 1 pm Eastern

Part 6: Azure Container Registry | Azure Container Instance

Azure Container Registry is a managed Docker registry service based on the open-source Docker Registry 2.0, which allows you to create and maintain Azure container registries to store and manage your private Docker container images. Azure Container Instance offers the fastest and simplest way to run a container in Azure, without having to provision any virtual machines and without having to adopt a higher-level service. You’ll learn about both ACR and ACI, and how they work closely together. After the session you’ll be able to complete the lab where you will deploy Azure Container Registry, use Azure Container Instance, and run your containerized workload.

April 26, 2019 10 am Pacific / 1 pm Eastern

Part 7: Container orchestration with Azure Container Services and Azure Kubernetes Services

This session provides a deep dive view on working with container orchestration in Azure and covers both Azure Container Services (ACS) and Azure Kubernetes Services (AKS). We’ll cover the similarities, differences, and roadmap of both, as well as walking through several typical container orchestrator tasks. To prepare you for the lab where you will deploy ACS with Kubernetes and deploy AKS, we’ll present detailed demos and provide samples for managing and deploying. You’ll also see a demo of running a Docker Hub image in AKS.

May 1, 2019 10 am Pacific / 1 pm Eastern

Part 8: Managing and monitoring Azure Kubernetes Services

You’ll learn enabling container scalability in AKS, monitoring AKS, and using Kubernetes dashboard with AKS. We’ll present lots of samples and detailed demos for running a Container Registry Image inside Azure Container Services, scaling AKS, and monitoring AKS in Azure. For the final lab in this workshop series, you will get hands on managing and monitoring AKS.

May 3, 2019 10 am Pacific / 1 pm Eastern

All sessions will be recorded and available for on demand viewing after they are delivered live, and the labs and other materials will be available on GitHub.
Quelle: Azure

15. April 2019

da Agency

Machine learning: A key weapon in the cybersecurity battle

Since the dawn of the internet, companies have been fighting to stay ahead of cybercriminals. Artificial intelligence (AI) and machine learning have made this job easier by automating complex processes for detecting attacks and reacting to breaches. However, cybercriminals are also using this technology for their own malicious purposes.
More and more hackers are exploiting cognitive technologies to spy, take control of Internet of Things (IoT) devices and carry out malicious activities. CSO magazine called 2018 “the year of the AI-powered cyberattack”. For example, smart malware bots are now using AI to collect data from thousands of breached devices and can learn from that information to make future attacks more difficult to prevent and detect.
As hackers weaponize AI, cybersecurity professionals must fight fire with fire by using cognitive technology to identify and prevent attacks.
Sophisticated phishing at scale
Neural networks, modeled after the human brain, can be used to automate “spear phishing”, the creation of phishing emails or tweets that are highly personal and target specific users. According to research by Blackhat, automated spear phishing had between a 30 to 66 percent success rate, which is 5 to 14 percent higher than large-scale traditional phishing campaigns and comparable with manual spear phishing campaigns.
Automation enables attackers to run spear phishing campaigns at an alarmingly large scale. However, companies are using the capabilities of AI as a countermeasure.
According to a recent Ponemon study, 52 percent of companies are looking to add in-house AI talent to help them boost their cybersecurity efforts, and 60 percent said AI could provide deeper security than purely human efforts. That’s why new security solutions such as IBM QRadar use machine learning to automate the threat detection process, helping cyber incident investigation and response efforts get started as much as 50 times faster than before.
CAPTCHA and authentication concerns
Another area in which AI tools are already helping cybercriminals do their dirty work is in breaking complex codes, whether it’s CAPTCHA or usernames and passwords. Using processes such as optical character recognition, the software can identify and learn from millions of images, eventually gaining the ability to recognize and solve a CAPTCHA. Similarly, hackers are applying the same optical character recognition combined with the ability to automate login requests to test stolen usernames and passwords across multiple sites.
Fighting back against such large-scale attacks requires leaning on these same AI technologies. One way to do this is to use learning-enabled technology to understand what is normal for a system, then flag unusual incidents for human review. Security professionals need AI-based monitoring solutions to provide automated help and identify which alerts pose a real and immediate risk.
Malware
Smart malware, which “learns” how to become less detectable, is also posing a significant threat. Defeating normal malware is typically done by “capturing” the malware and reverse engineering it to figure out how it works. However, in smart malware, it is more difficult to analyze how the neural network makes decisions on who to attack.
While reverse-engineering smart malware remains challenging, neural networks have been successful at recognizing malicious domains created by a domain generation algorithm (DGA), which creates pseudo-random domain names. A smart DGA keeps changing to stay ahead of attempts to thwart it, but, likewise, a smart neural network will continue to learn the strategies deployed by hackers and how to defeat them.
Fight security threats before they happen
One of the most powerful aspects of security enabled by AI and machine learning is the ability to uncover patterns and learn from unstructured data. As a result, these tools can provide security professionals with the means to combat attacks, as well as insights into emerging threats and recommendations on how to defend against impending incidents. Additionally, machine learning can help locate vulnerabilities that may be difficult for human security teams to find.
Cybercriminals are already using AI to launch larger-scale, more sophisticated attacks. Here’s the good news: companies can fight back by using these same technologies. If your organization has been considering implementing AI but hasn’t yet put a plan in place, the time is now, and the business case has arrived. Cognitive technologies such as neural networks and automated security monitoring solutions can help bring your business’s defenses into the cyber age and give you the most cutting-edge weapons to defend against emerging threats.
Discover the ways that IBM Cloud Private for Data can enable security by supporting the development and deployment of AI and machine learning capabilities.
The post Machine learning: A key weapon in the cybersecurity battle appeared first on Cloud computing news.
Quelle: Thoughts on Cloud