Monat: Februar 2020

Fileless attack detection for Linux in preview

This blog post was co-authored by Aditya Joshi, Senior Software Engineer, Enterprise Protection and Detection.

Attackers are increasingly employing stealthier methods to avoid detection. Fileless attacks exploit software vulnerabilities, inject malicious payloads into benign system processes, and hide in memory. These techniques minimize or eliminate traces of malware on disk, and greatly reduce the chances of detection by disk-based malware scanning solutions.

To counter this threat, Azure Security Center released fileless attack detection for Windows in October 2018. Our blog post from 2018 explains how Security Center can detect shellcode, code injection, payload obfuscation techniques, and other fileless attack behaviors on Windows. Our research indicates the rise of fileless attacks on Linux workloads as well.

Today, Azure Security Center is happy to announce a preview for detecting fileless attacks on Linux.  In this post, we will describe a real-world fileless attack on Linux, introduce our fileless attack detection capabilities, and provide instructions for onboarding to the preview. 

Real-world fileless attack on Linux

One common pattern we see is attackers injecting payloads from packed malware on disk into memory and deleting the original malicious file from the disk. Here is a recent example:

An attacker infects a Hadoop cluster by identifying the service running on a well-known port (8088) and uses Hadoop YARN unauthenticated remote command execution support to achieve runtime access on the machine. Note, the owner of the subscription could have mitigated this stage of the attack by configuring Security Center JIT.
The attacker copies a file containing packed malware into a temp directory and launches it.
The malicious process unpacks the file using shellcode to allocate a new dynamic executable region of memory in the process’s own memory space and injects an executable payload into the new memory region.
The malware then transfers execution to the injected ELF entry point.
The malicious process deletes the original packed malware from disk to cover its tracks. 
The injected ELF payload contains a shellcode that listens for incoming TCP connections, transmitting the attacker’s instructions.

This attack is difficult for scanners to detect. The payload is hidden behind layers of obfuscation and only present on disk for a short time.  With the fileless attack detection preview, Security Center can now identify these kinds of payloads in memory and inform users of the payload’s capabilities.

Fileless attacks detection capabilities

Like fileless attack detection for Windows, this feature scans the memory of all processes for evidence of fileless toolkits, techniques and behaviors. Over the course of the preview, we will be enabling and refining our analytics to detect the following behaviors of userland malware:

Well known toolkits and crypto mining software. 
Shellcode, injected ELF executables, and malicious code in executable regions of process memory.
LD_PRELOAD based rootkits to preload malicious libraries.
Elevation of privilege of a process from non-root to root.
Remote control of another process using ptrace.

In the event of a detection, you receive an alert in the Security alerts page. Alerts contain supplemental information such as the kind of techniques used, process metadata, and network activity. This enables analysts to have a greater understanding of the nature of the malware, differentiate between different attacks, and make more informed decisions when choosing remediation steps.

 

The scan is non-invasive and does not affect the other processes on the system.  The vast majority of scans run in less than five seconds. The privacy of your data is protected throughout this procedure as all memory analysis is performed on the host itself. Scan results contain only security-relevant metadata and details of suspicious payloads.

Getting started

To sign-up for this specific preview, or our ongoing preview program, indicate your interest in the "Fileless attack detection preview."

Once you choose to onboard, this feature is automatically deployed to your Linux machines as an extension to Log Analytics Agent for Linux (also known as OMS Agent), which supports the Linux OS distributions described in this documentation. This solution supports Azure, cross-cloud and on-premise environments. Participants must be enrolled in the Standard or Standard Trial pricing tier to benefit from this feature.

To learn more about Azure Security Center, visit the Azure Security Center page.
Quelle: Azure

Burst 4K encoding on Azure Kubernetes Service

Burst encoding in the cloud with Azure and Media Excel HERO platform.

Content creation has never been as in demand as it is today. Both professional and user-generated content has increased exponentially over the past years. This puts a lot of stress on media encoding and transcoding platforms. Add the upcoming 4K and even 8K to the mix and you need a platform that can scale with these variables. Azure Cloud compute offers a flexible way to grow with your needs. Microsoft offers various tools and products to fully support on-premises, hybrid, or native cloud workloads. Azure Stack offers support to a hybrid scenario for your computing needs and Azure ARC helps you to manage hybrid setups.

Finding a solution

Generally, 4K/UHD live encoding is done on dedicated hardware encoder units, which cannot be hosted in a public cloud like Azure. With such dedicated hardware units hosted on-premise that need to push 4K into the Azure data center the immediate problem we face is a need for high bandwidth network connection between the encoder unit on-premise and Azure data center. In general, it's a best practice to ingest into multiple regions, increasing the load on the network connected between the encoder and the Azure Datacenter.

How do we ingest 4K content reliably into the public cloud?

Alternatively, we can encode the content in the cloud. If we can run 4K/UHD live encoding in Azure, its output can be ingested into Azure Media Services over the intra-Azure network backbone which provides sufficient bandwidth and reliability.

How can we reliably run and scale 4K/UHD live encoding on the Azure cloud as a containerized solution? Let's explore below. 

Azure Kubernetes Service

With Azure Kubernetes Services (AKS) Microsoft offers a managed Kubernetes platform to customers. It is a hosted Kubernetes platform without having to spend a lot of time creating a cluster with all the necessary configuration burden like networking, cluster masters, and OS patching of the cluster nodes. It also comes with pre-configured monitoring seamlessly integrating with Azure Monitor and Log Analytics. Of course, it still offers flexibility to integrate your own tools. Furthermore, it is still just the plain vanilla Kubernetes and as such is fully compatible with any existing tooling you might have running on any other standard Kubernetes platform.

Media Excel encoding

Media Excel is an encoding and transcoding vendor offering physical appliance and software-based encoding solutions. Media Excel has been partnering with Microsoft for many years and engaging in Azure media customer projects. They are also listed as recommended and tested contribution encoder for Azure Media Services for fMP4. There has also work done by both Media Excel and Microsoft to integrate SCTE-35 timed metadata from Media Excel encoder to an Azure Media Services Origin supporting Server-Side Ad Insertion (SSAI) workflows.

Networking challenge

With increasing picture quality like 4K and 8K, the burden on both compute and networking becomes a significant architecting challenge. In a recent engagement with a customer, we needed to architect a 4K live streaming platform with a challenge of limited bandwidth capacity from the customer premises to one of our Azure Datacenters. We worked with Media Excel to build a scalable containerized encoding platform on AKS. Utilizing cloud compute and minimizing network latency between Encoder and Azure Media Services Packager. Multiple bitrates with a top bitrate up to 4Kp60@20Mbps of the same source are generated in the cloud and ingested into the Azure Media Services platform for further processing. This includes Dynamic Encryption and Packaging. This setup enables the following benefits:

Instant scale to multiple AKS nodes
Eliminate network constraints between customer and Azure Datacenter
Automated workflow for containers and easy separation of concern with container technology
Increased level of security of high-quality generated content to distribution
Highly redundant capability
Flexibility to provide various types of Node pools for optimized media workloads

In this particular test, we proved that the intra-Azure network is extremely capable of shipping high bandwidth, latency-sensitive 4K packets from a containerized encoder instance running in West Europe to both East US and Honk Kong Datacenter Regions. This allows the customer to place origin closer to them for further content conditioning.

Workflow:

Azure Pipeline is triggered to deploy onto the AKS cluster. In the YAML file (which you can find on Github) there is a reference to the Media Excel Container in Azure Container Registry.
AKS starts deployment and pulls container from Azure Container Registry.
During Container start custom PHP script is loaded and container is added to the HMS (Hero Management Service). And placed into the correct device pool and job.
Encoder loads source and (in this case) push 4K Livestream into Azure Media Services.
Media Services packaged Livestream into multiple formats and apply DRM (digital rights management).
Azure Content Deliver Network scales livestream.

Scale through Azure Container Instances

With Azure Kubernetes Services you get the power of Azure Container Instances out of the box. Azure Container Instances are a way to instantly scale to pre-provisioned compute power at your disposal. When deploying Media Excel encoding instances to AKS you can specify where these instances will be created. This offers you the flexibility to work with variables like increased density on cheaper nodes for low-cost low priority encoding jobs or more expensive nodes for high throughput high priority jobs. With Azure Container Instances you can instantly move workloads to standby compute power without provisioning time. You only pay for the compute time offering full flexibility for customer demand and future change in platform needs. With Media Excel’s flexible Live/File based encoding roles you can easily move workloads across different compute power offered by AKS and Azure Container Instances.

Azure DevOps pipeline to bring it all together

All the general benefits that come with containerized workload apply in the following case. For this particular proof-of-concept, we created an automated deployment pipeline in Azure DevOps for easy testing and deployment. With a deployment YAML and Pipeline YAML we can easily automate deployment, provisioning and scaling of a Media Excel encoding container. Once DevOps pushes the deployment job onto AKS a container image is pulled from Azure Container Registry. Although container images can be bulky utilizing node side caching of layers any additional container pull is greatly improved down to seconds. With the help of Media Excel, we created a YAML file container pre- and post-container lifecycle logic that will add and remove a container from Media Excel's management portal. This offers an easy single pane of glass management of multiple instances across multiple node types, clusters, and regions.

This deployment pipeline offers full flexibility to provision certain multi-tenant customers or job priority on specific node types. This unlocks the possibility of provision encoding jobs on GPU enabled nodes for maximum throughput or using cheaper generic nodes for low priority jobs.

Azure Media Services and Azure Content Delivery Network

Finally, we push the 4K stream into Azure Media Services. Azure Media Services is a cloud-based platform that enables you to build solutions that achieve broadcast-quality video streaming, enhance accessibility and distribution, analyze content, and much more. Whether you're an app developer, a call center, a government agency, or an entertainment company, Media Services helps you create apps that deliver media experiences of outstanding quality to large audiences on today’s most popular mobile devices and browsers.

Azure Media Services is seamlessly integrated with Azure Content Delivery Network. With Azure Content Delivery Network we offer a true multi CDN with choices of Azure Content Delivery Network from Microsoft, Azure Content Delivery Network from Verizon, and Azure Content Delivery Network from Akamai. All of this through a single Azure Content Delivery Network API for easy provisioning and management. As an added benefit, all CDN traffic between Azure Media Services Origin and CDN edge is free of charge.

With this setup, we’ve demonstrated that Cloud encoding is ready to handle real-time 4K encoding across multiple clusters. Thanks to Azure services like AKS, Container Registry, Azure DevOps, Media Services, and Azure Content Delivery Network, we demonstrated how easy it is to create an architecture that is capable of meeting high throughput time-sensitive constraints.
Quelle: Azure