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Archive for December, 2017

How Microsoft tools and partners support GDPR compliance

This post is authored by Daniel Grabski,Executive Security Advisor, Microsoft Enterprise Cybersecurity Group.

As an Executive Security Advisor for enterprises in Europe and the Middle East, I regularly engage with Chief Information Security Officers (CISOs), Chief Information Officers (CIOs) and Data Protection Officers (DPOs) to discuss their thoughts and concerns regarding the General Data Protection Regulation, or GDPR. In my last post about GDPR, I focused on how GDPR is driving the agenda of CISOs. This post will present resources to address these concerns.

Some common questions are How can Microsoft help our customers to be compliant with GDPR? and, Does Microsoft have tools and services to support the GDPR journey? Another is, How can I engage current investments in Microsoft technology to address GDPR requirements?

To help answer these, I will address the following:

  • GDPR benchmark assessment tool
  • Microsoft partners & GDPR
  • Microsoft Compliance Manager
  • New features in Azure Information Protection

Tools for CISOs

There are tools available that can ease kick-off activities for CISOs, CIOs, and DPOs. These tools can help them better understand their GDPR compliance, including which areas are most important to be improved.

  • To begin, Microsoft offers a free GDPR benchmark assessment tool which is available online to any business or organization.The assessment questions are designed to assist our customers to identify technologies and steps that can be implemented to simplify GDPR compliance efforts. It is also a tool allowing increased visibility and understanding of features available in Microsoft technologies that may already be available in existing infrastructures. The tool can reveal what already exists and what is not addressed to support each GDPR journey. As an outcome of the assessment, a full report is sentan example of which is shown here.

Image 1: GDPR benchmarking tool

As an example, see below the mapping to the first question in the Assessment. This is based on how Microsoft technology can support requirements about collection, storage, and usage of personal data; it is necessary to first identify the personal data currently held.

  • Azure Data Catalog provides a service in which many common data sources can be registered, tagged, and searched for personal data. Azure Search allows our customers to locate data across user-defined indexes. It is also possible to search for user accounts in Azure Active Directory. For example, CISOs can use the Azure Data Catalog portal to remove preview data from registered data assets and delete data assets from the catalog:

Image 2: Azure Data Catalogue

  • Dynamics 365 provides multiple methods to search for personal data within records such as Advanced Find, Quick Find, Relevance Search, and Filters. These functions each enable the identification of personal data.
  • Office 365 includes powerful tools to identify personal data across Exchange Online, SharePoint Online, OneDrive for Business, and Skype for Business environments. Content Search allows queries for personal data using relevant keywords, file properties, or built-in templates. Advanced eDiscovery identifies relevant data faster, and with better precision, than traditional keyword searches by finding near-duplicate files, reconstructing email threads, and identifying key themes and data relationships. Image 3 illustrates the common workflow for managing and using eDiscovery cases in the Security & Compliance Center and Advanced eDiscovery.

Image 3: Security & Compliance Center and Advanced eDiscovery

  • Windows 10 and Windows Server 2016 have tools to locate personal data, including PowerShell, which can find data housed in local and connected storage, as well as search for files and items by file name, properties, and full-text contents for some common file and data types.

A sample outcome, based on one of the questions regarding GDPR requirements, as shown in Image 4.

Image 4: example of the GDPR requirements mapped with features in the Microsoft platform

Resources for CISOs

Microsofts approach to GDPR relies heavily on working together with partners. Therefore, we built a broader version of the GDPR benchmarking tool available to customers through the extensive Microsoft Partner Network. The tool provides an in-depth analysis of an organizations readiness and offers actionable guidance on how to prepare for compliance, including how Microsoft products and features can help simplify the journey.

The Microsoft GDPR Detailed Assessmentis intended to be used by Microsoft partners who are assisting customers to assess where they are on their journey to GDPR readiness. The GDPR Detailed Assessment is accompanied by supporting materials to assist our partners in facilitating customer assessments.

In a nutshell, the GDPR Detailed Assessment is a three-step process where Microsoft partners engage with customers to assess their overall GDPR maturity. Image 5 below presents a high-level overview of the steps.

Image 5

The duration for the partner engagement is expected to last 3-4 weeks, while the total effort is estimated to be 10 to 20 hours, depending on the complexity of the organization and the number of participants as you can see below.

Image 6: Duration of the engagement

The Microsoft GDPR Detailed Assessment is intended for use by Microsoft partners to assess their customers overall GDPR maturity. It is not offered as a GDPR compliance attestation. Customers are responsible to ensure their own GDPR compliance and are advised to consult their legal and compliance teams for guidance. This tool is intended to highlight resources that can be used by partners to support a customers journey towards GDPR compliance.

We are all aware that achieving organizational compliance may be challenging. It is hard to stay up-to-date with all the regulations that matter to organizations and to define and implement controls with limited in-house capability.

To address these challenges, Microsoft announced a new compliance solution to help organizations meet data protection and regulatory standards more easily when using Microsoft cloud services Compliance Manager. The preview program, available today, addresses compliance management challenges and:

  • Enables real-time risk assessment on Microsoft cloud services
  • Provides actionable insights to improve data protection capabilities
  • Simplifies compliance processes through built-in control management and audit-ready reporting tools

Image 7 shows a dashboard summary illustrating a compliance posture against the data protection regulatory requirements that matter when using Microsoft cloud services. The dashboard summarizes Microsofts and your performance on control implementation on various data protection standards and regulations, including GDPR, ISO 27001, and ISO 27018.

Image 7: Compliance Manager dashboard

Having a holistic view is just the beginning. Use the rich insights available in Compliance Manager to go deeper to understand what should be done and improved. Each Microsoft-managed control illuminates the implementation and testing details, test date, and results. The tool provides recommended actions with step-by-step guidance. It aides better understanding of how to use the Microsoft cloud features to efficiently implement the controls managed by your organization. Image 8 shows an example of the insight provided by the tool.

Image 8: Information to help you improve your data protection capabilities

During the recentMicrosoft Ignite conference, Microsoft announced Azure Information Protection scanner. The feature is now available in public preview. This will help to manage and protect significant on-premise data and help prepare our customers and partners for regulations such as GDPR.

We released Azure Information Protection (AIP) to provide the ability to define a data classification taxonomy and apply those business rules to emails and documents. This feature is critical to protecting the data correctly throughout the lifecycle, regardless of where it is stored or shared.

We receive a lot of questions about how Microsoft can help to discover, label, and protect existing files to ensure all sensitive information is appropriately managed. The AIP scanner can:

  • Discover sensitive data that is stored in existing repositories when planning data-migration projects to cloud storage, to ensure toxic data remains in place.
  • Locate data that includes personal data and learn where it is stored to meet regulatory and compliance needs
  • Leverage existing metadata that was applied to files using other solutions

I encourage you to enroll for the preview version of Azure Information Protection scanner and to continue to grow your knowledge about how Microsoft is addressing GDPR and general security with these helpful resources:


About the author:

Daniel Grabski is a 20-year veteran of the IT industry, currently serving as an Executive Security Advisor for organizations in Europe, the Middle East, and Africa with Microsoft Enterprise Cybersecurity Group. In this role he focuses on enterprises, partners, public sector customers and critical infrastructure stakeholders delivering strategic security expertise, advising on cybersecurity solutions and services needed to build and maintain secure and resilient ICT infrastructure.

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How public-private partnerships can combat cyber adversaries

December 13th, 2017 No comments

For several years now, policymakers and practitioners from governments, CERTs, and the security industry have been speaking about the importance of public-private partnerships as an essential part of combating cyber threats. It is impossible to attend a security conference without a keynote presenter talking about it. In fact, these conferences increasingly include sessions or entire tracks dedicated to the topic. During the three conferences Ive attended since Junetwo US Department of Defense symposia, and NATOs annual Information Symposium in Belgium, the message has been consistent: public-private information-sharing is crucial to combat cyber adversaries and protect users and systems.

Unfortunately, we stink at it. Information-sharing is the Charlie Brown football of cyber: we keep running toward it only to fall flat on our backs as attackers continually pursue us. Just wait til next year. Its become easier to talk about the need to improve information-sharing than to actually make it work, and its now the technology industrys convenient crutch. Why? Because no one owns it, so no one is accountable. I suspect we each have our own definition of what information-sharing means, and of what success looks like. Without a sharp vision, can we really expect it to happen?

So, what can be done?

First, some good news: the security industry wants to do this–to partner with governments and CERTs. So, when we talk about it at conferences, or when a humble security advisor in Redmond blogs about it, its because we are committed to finding a solution. Microsoft recently hosted BlueHat, where hundreds of malware hunters, threat analysts, reverse engineers, and product developers from the industry put aside competitive priorities to exchange ideas and build partnerships. In my ten years with Microsoft, Ive directly participated in and led information-sharing initiatives that we established for the very purpose of advancing information assurance and protecting cyberspace. In fact, in 2013, Microsoft created a single legal and programmatic framework to address this issue, the Government Security Program.

For the partnership to work, it is important to understand and anticipate the requirements and needs of government agencies. For example, we need to consider cyber threat information, YARA rules, attacker campaign details, IP address, host, network traffic, and the like.

What can governments and CERTs do to better partner with industry?

  • Be flexible, especially on the terms. Communicate. Prioritize. In my experience, the mean-time-to-signature for a government to negotiate an info-sharing agreement with Microsoft is between six months and THREE YEARS.
  • Prioritize information sharing. If this is already a priority, close the gap. I fear governments attorneys are not sufficiently aware of how important the agreements are to their constituents. The information-sharing agreements may well be non-traditional agreements, but if information-sharing is truly a priority, lets standardize and expedite the agreements. Start by reading the 6 Nov Department of Homeland Security OIG report, DHS Can Improve Cyber Threat Information-Sharing document.
  • Develop and share with industry partners a plan to show how government agencies will consume and use our data. Let industry help government and CERTs improve our collective ROI. Before asking for data, lets ensure it will be impactful.
  • Develop KPIs to measure whether an information-sharing initiative is making a difference, quantitative or qualitative. In industry, we could do a better job at this, as we generally assume that were providing information for the right reason. However, I frequently question whether our efforts make a real difference. Whether we look for mean-time-to-detection improvements or other metrics, this is an area for improvement.
  • Commit to feedback. Public-private information-sharing implies two-way communication. Understand that more companies are making feedback a criterion to justify continuing investment in these not-for-profit engagements. Feedback helps us justify up the chain the efficacy of efforts that we know are important. It also improves two-way trust and contributes to a virtuous cycle of more and closer information-sharing. At Microsoft, we require structured feedback as the price of entry for a few of our programs.
  • Balance interests in understanding todays and tomorrows threats with an equal commitment to lock down what is currently owned.(My favorite) Information-sharing usually includes going after threat actors and understanding whats coming next. Thats important, but in an assume compromise environment, we need to continue to hammer on the basics:

    • Patch.If an integrator or on-site provider indicates patching and upgrading will break an application, and if that is used as an excuse not to patch, that is a problem. Authoritative third-parties such as US-CERT, SANS, and others recommend a 48- to 72-hour patch cycle. Review www.microsoft.com/secure to learn more.

      • Review www.microsoft.com/sdl to learn more about tackling this issue even earlier in the IT development cycle, and how to have important conversations with contractors, subcontractors,and ISVs in the software and services supply chain.

    • Reduce administrative privilege. This is especially important for contractor or vendor accounts. Up to 90 percent of breaches come from credential compromise. This is largely caused by a lack of, or obsolete, administrative, physical and technical controls to sensitive assets. Basic information-sharing demands that we focus on this. Here is guidance regarding securing access.

Ultimately, we in the industry can better serve governments and CERTs by incentivizing migrations to newer platforms which offer more built-in security; and that are more securely developed. As we think about improving information-sharing, lets be clear that this includes not only sharing technical details about threats and actors but also guidance on making governments fundamentally more secure on newer and more secure technologies.

 

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4056318 – Guidance for securing AD DS account used by Azure AD Connect for directory synchronization – Version: 1.0

Revision Note: V1.0 (December 12, 2017): Advisory published.
Summary: Microsoft is releasing this security advisory to provide information regarding security settings for the AD DS (Active Directory Domain Services) account used by Azure AD Connect for directory synchronization. This advisory also provides guidance on what on-premises AD administrators can do to ensure that the account is properly secured.

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4053440 – Securely opening Microsoft Office documents that contain Dynamic Data Exchange (DDE) fields – Version: 2.0

Revision Note: V2.0 (December 12, 2017): Microsoft has released an update for all supported editions of Microsoft Word that allows users to set the functionality of the DDE protocol based on their environment. For more information and to download the update, see ADV170021.
Summary: Microsoft is releasing this security advisory to provide information regarding security settings for Microsoft Office applications. This advisory provides guidance on what users can do to ensure that these applications are properly secured when processing Dynamic Data Exchange (DDE) fields.

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4056318 – Guidance for securing AD DS account used by Azure AD Connect for directory synchronization – Version: 1.0

Revision Note: V1.0 (December 12, 2017): Advisory published.
Summary: Microsoft is releasing this security advisory to provide information regarding security settings for the AD DS (Active Directory Domain Services) account used by Azure AD Connect for directory synchronization. This advisory also provides guidance on what on-premises AD administrators can do to ensure that the account is properly secured.

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4053440 – Securely opening Microsoft Office documents that contain Dynamic Data Exchange (DDE) fields – Version: 2.0

Revision Note: V2.0 (December 12, 2017): Microsoft has released an update for all supported editions of Microsoft Word that allows users to set the functionality of the DDE protocol based on their environment. For more information and to download the update, see ADV170021.
Summary: Microsoft is releasing this security advisory to provide information regarding security settings for Microsoft Office applications. This advisory provides guidance on what users can do to ensure that these applications are properly secured when processing Dynamic Data Exchange (DDE) fields.

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4056318 – Guidance for securing AD DS account used by Azure AD Connect for directory synchronization – Version: 1.0

Revision Note: V1.0 (December 12, 2017): Advisory published.
Summary: Microsoft is releasing this security advisory to provide information regarding security settings for the AD DS (Active Directory Domain Services) account used by Azure AD Connect for directory synchronization. This advisory also provides guidance on what on-premises AD administrators can do to ensure that the account is properly secured.

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Detonating a bad rabbit: Windows Defender Antivirus and layered machine learning defenses

December 11th, 2017 No comments

Windows Defender Antivirus uses a layered approach to protection: tiers of advanced automation and machine learning models evaluate files in order to reach a verdict on suspected malware. While Windows Defender AV detects a vast majority of new malware files at first sight, we always strive to further close the gap between malware release and detection.

In a previous blog post, we looked at a real-world case study showing how Windows Defender Antivirus cloud protection service leverages next-gen security technologies to save “patient zero” from new malware threats in real-time. In that case study, a new Spora ransomware variant was analyzed and blocked within seconds using a deep neural network (DNN) machine learning classifier in the cloud. In this blog post well look at how additional automated analysis and machine learning models can further protect customers within minutes in rare cases where initial classification is inconclusive.

Layered machine learning models

In Windows Defender AVs layered approach to defense, if the first layer doesnt detect a threat, we move on to the next level of inspection. As we move down the layers, the amount of time required increases. However, we catch the vast majority of malware at the first (fastest) protection layers and only need to move on to a more sophisticated (but slower) level of inspection for rarer/more advanced threats.

For example, the vast majority of scanned objects are evaluated by the local Windows Defender client machine learning models, behavior-based detection algorithms, generic and heuristic classifications, and more. This helps ensure that users get the best possible performance. In rare cases where local intelligence cant reach a definitive verdict, Windows Defender AV will use the cloud for deeper analysis.

Figure 1. Layered detection model

For a more detailed look at our approach to protection, see The evolution of malware prevention.

Detonation-based machine learning classification

We use a variety of machine learning models that use different algorithms to predict whether a certain file is malware. Some of these algorithms are binary classifiers that give a strict clean-or-malware verdict (0 or 1), while others are multi-class classifiers that provide a probability for each classification (malware, clean, potentially unwanted application, etc). Each machine learning model is trained against a set of different features (often thousands, sometimes hundreds of thousands) to learn to distinguish between different kinds of programs.

For the fastest classifiers in our layered stack, the features may include static attributes of the file combined with events (for example, API calls or behaviors) seen while the scanning engine emulates the file using dynamic translation. If the results from these models are inconclusive, well take an even more in-depth look at what the malware does by actually executing it in a sandbox and observing its run-time behavior. This is known as dynamic analysis, or detonation, and happens automatically whenever we receive a new suspected malware sample.

The activities seen in the sandbox machine (for example, registry changes, file creation/deletion, process injection, network connections, and so forth) are recorded and provided as features to our ML models. These models can then combine both the static features obtained from scanning the file with the dynamic features observed during detonation to arrive at an even stronger prediction.

Figure 2. Detonation-based machine learning classification

Ransom:Win32/Tibbar.A Protection in 14 minutes

On October 24, 2017, in the wake of recent ransomware outbreaks such as Wannacry and NotPetya, news broke of a new threat spreading, primarily in Ukraine and Russia: Ransom:Win32/Tibbar.A (popularly known as Bad Rabbit).

This threat is a good example of how detonation-based machine learning came into play to protect Windows Defender AV customers. First though, lets look at what happened to patient zero.

At 11:17 a.m. local time on October 24, a user running Windows Defender AV in St. Petersburg, Russia was tricked into downloading a file named FlashUtil.exe from a malicious website. Instead of a Flash update, the program was really the just-released Tibbar ransomware.

Windows Defender AV scanned the file and determined that it was suspicious. A query was sent to the cloud protection service, where several metadata-based machine learning models found the file suspicious, but not with a high enough probability to block. The cloud protection service requested that Windows Defender AV client to lock the file, upload it for processing, and wait for a decision.

Within a few seconds the file was processed, and sample-analysis-based ML models returned their conclusions. In this case, a multi-class deep neural network (DNN) machine learning classifier correctly classified the Tibbar sample as malware, but with only an 81.6% probability score. In order to avoid false positives, cloud protection service is configured by default to require at least 90% probability to block the malware (these thresholds are continually evaluated and fine-tuned to find the right balance between blocking malware while avoiding the blocking of legitimate programs). In this case, the ransomware was allowed to run.

Figure 3. Ransom:Win32/Tibbar.A ransom note

Detonation chamber

In the meantime, while patient zero and eight other unfortunate victims (in Ukraine, Russia, Israel, and Bulgaria) contemplated whether to pay the ransom, the sample was detonated and details of the system changes made by the ransomware were recorded.

Figure 4. Sample detonation events used by the machine learning model

As soon as the detonation results were available, a multi-class deep neural network (DNN) classifier that used both static and dynamic features evaluated the results and classified the sample as malware with 90.7% confidence, high enough for the cloud to start blocking.

When a tenth Windows Defender AV customer in the Ukraine was tricked into downloading the ransomware at 11:31 a.m. local time, 14 minutes after the first encounter, cloud protection service used the detonation-based malware classification to immediately block the file and protect the customer.

At this point the cloud protection service had “learned” that this file was malware. It now only required metadata from the client with the hash of the file to issue blocking decisions and protect customers. As the attack gained momentum and began to spread, Windows Defender AV customers with cloud protection enabled were protected. Later, a more specific detection was released to identify the malware as Ransom:Win32/Tibbar.A.

Closing the gap

While we feel good about Windows Defender AV’s layered approach to protection, digging deeper and deeper with automation and machine learning in order to finally reach a verdict on suspected malware, we are continually seeking to close the gap even further between malware release and protection. The cases where we cannot block at first sight are increasingly rare, but there is so much to be done. As our machine learning models are continuously updated and retrained, we are able to make better predictions over time. Yet malware authors will not rest, and the ever-changing threat landscape requires continuous investment in new and better technologies to detect new threats, but also to effectively differentiate the good from the bad.

What about systems that do get infected while detonation and classification are underway? One area that we’re actively investing in is advanced remediation techniques that will let us reach back out to those systems in an organization that were vulnerable and, if possible, get them back to a healthy state.

If you are organization that is willing to accept a higher false positive risk in exchange for stronger protection, you can configure the cloud protection level to tell the Windows Defender AV cloud protection service to take a more aggressive stance towards suspicious files, such as blocking at lower machine learning probability thresholds. In the Tibbar example above, for example, a configuration like this could have protected patient zero using the initial 81% confidence score, and not wait for the higher confidence (detonation-based) result that came later. You can also configure the cloud extended timeout to give the cloud protection service more time to evaluate a first-seen threat.

As another layer of real-time protection against ransomware, enable Controlled folder access, which is one of the features of the new Windows Defender Exploit Guard. Controlled folder access protects files from tampering by locking folders so that ransomware and other unauthorized apps cant access them.

For enterprises, Windows Defender Exploit Guards other features (Attack Surface Reduction, Exploit protection, and Network protection) further protect networks from advanced attacks. Windows Defender Advanced Threat Protection can also alert security operations personnel about malware activities in the network so that personnel can promptly investigate and respond to attacks.

For users running Windows 10 S, malware like Tibbar simply wont run. Windows 10 S provides advanced levels of security by exclusively running apps from the Microsoft Store. Threats such as Tibbar are non-issues for Windows 10 S users. Learn more about Windows 10 S.

New machine learning and AI techniques, in combination with both static and dynamic analysis, gives Windows Defender AV the ability to block more and more malware threats at first sight and, if that fails, learn as quickly as possible that something is bad and start blocking it. Using a layered approach, with different ML models at each layer, gives us the ability to target a wide variety of threats quickly while maintaining low false positive rates. As we gather more data about a potential threat, we can provide predictions with higher and higher confidence and take action accordingly. It is an exciting time to be in the fray.

 

Randy Treit

Senior Security Researcher, Windows Defender Research

 

 


Talk to us

Questions, concerns, or insights on this story? Join discussions at the Microsoft community and Windows Defender Security Intelligence.

Follow us on Twitter @WDSecurity and Facebook Windows Defender Security Intelligence.

 

Microsoft teams up with law enforcement and other partners to disrupt Gamarue (Andromeda)

December 4th, 2017 No comments

Today, with help from Microsoft security researchers, law enforcement agencies around the globe, in cooperation with Microsoft Digital Crimes Unit (DCU), announced the disruption of Gamarue, a widely distributed malware that has been used in networks of infected computers collectively called the Andromeda botnet.

The disruption is the culmination of a journey that started in December 2015, when the Microsoft Windows Defender research team and DCU activated a Coordinated Malware Eradication (CME) campaign for Gamarue. In partnership with internet security firm ESET, we performed in-depth research into the Gamarue malware and its infrastructure.

Our analysis of more than 44,000 malware samples uncovered Gamarues sprawling infrastructure. We provided detailed information about that infrastructure to law enforcement agencies around the world, including:

  • 1,214 domains and IP addresses of the botnets command and control servers
  • 464 distinct botnets
  • More than 80 associated malware families

The coordinated global operation resulted in the takedown of the botnets servers, disrupting one of the largest malware operations in the world. Since 2011, Gamarue has been distributing a plethora of other threats, including:

A global malware operation

For the past six years, Gamarue has been a very active malware operation that, until the takedown, showed no signs of slowing down. Windows Defender telemetry in the last six months shows Gamarues global prevalence.

Figure 1. Gamarues global prevalence from May to November 2017

While the threat is global, the list of top 10 countries with Gamarue encounters is dominated by Asian countries.

Figure 2. Top 10 countries with the most Gamarue encounters from May to November 2017

In the last six months, Gamarue was detected or blocked on approximately 1,095,457 machines every month on average.

Figure 3. Machines, IPs, and unique file encounters for Gamarue from May to November 2017; data does not include LNK detections

The Gamarue bot

Gamarue is known in the underground cybercrime market as Andromeda bot. A bot is a program that allows an attacker to take control of an infected machine. Like many other bots, Gamarue is advertised as a crime kit that hackers can purchase.

The Gamarue crime kit includes the following components:

  • Bot-builder, which builds the malware binary that infects computers
  • Command-and-control application, which is a PHP-based dashboard application that allows hackers to manage and control the bots
  • Documentation on how to create a Gamarue botnet

A botnet is a network of infected machines that communicate with command-and-control (C&C) servers, which are computer servers used by the hacker to control infected machines.

The evolution of the Gamarue bot has been the subject of many thorough analyses by security researchers. At the time of takedown, there were five known active Gamarue versions: 2.06, 2.07, 2.08, 2.09, and 2.10. The latest and the most active is version 2.10.

Gamarue is modular, which means that its functionality can be extended by plugins that are either included in the crime kit or available for separate purchase. The Gamarue plugins include:

  • Keylogger ($150) Used for logging keystrokes and mouse activity in order to steal user names and passwords, financial information, etc
  • Rootkit (included in crime kit) Injects rootkit codes into all processes running on a victim computer to give Gamarue persistence
  • Socks4/5 (included in crime kit) Turns victim computer into a proxy server for serving malware or malicious instructions to other computers on the internet
  • Formgrabber ($250) Captures any data submitted through web browsers (Chrome, Firefox, and Internet Explorer)
  • Teamviewer ($250) Enables attacker to remotely control the victim machine, spy on the desktop, perform file transfer, among other functions
  • Spreader Adds capability to spread Gamarue malware itself via removable drives (for example, portable hard drives or flash drives connected via a USB port); it also uses Domain Name Generation (DGA) for the servers where it downloads updates

Gamarue attack kill-chain

Over the years, various attack vectors have been used to distribute Gamarue. These include:

  • Removable drives
  • Social media (such as Facebook) messages with malicious links to websites that host Gamarue
  • Drive-by downloads/exploit kits
  • Spam emails with malicious links
  • Trojan downloaders

Once Gamarue has infected a machine, it contacts the C&C server, making the machine part of the botnet. Through the C&C server, the hacker can control Gamarue-infected machines, steal information, or issue commands to download additional malware modules.

Figure 4. Gamarues attack kill-chain

Gamarues main goal is to distribute other prevalent malware families. During the CME campaign, we saw at least 80 different malware families distributed by Gamarue. Some of these malware families include:

The installation of other malware broadens the scale of what hackers can do with the network of infected machines.

Command-and-control communication

When the Gamarue malware triggers the infected machine to contact the C&C server, it provides information like the hard disks volume serial number (used as the bot ID for the computer), the Gamarue build ID, the operating system of the infected machine, the local IP address, an indication whether the signed in user has administrative rights, and keyboard language setting for the infected machine. This information is sent to the C&C server via HTTP using the JSON format:

Figure 5. Information sent by Gamarue to C&C server

The information about keyboard language setting is very interesting, because the machine will not be further infected if the keyboard language corresponds to the following countries:

  • Belarus
  • Russia
  • Ukraine
  • Kazahkstan

Before sending to the C&C server, this information is encrypted with RC4 algorithm using a key hardcoded in the Gamarue malware body.

Figure 6. Encrypted C&C communication

Once the C&C server receives the message, it sends a command that is pre-assigned by the hacker in the control dashboard.

Figure 7. Sample control dashboard used by attackers to communicate to Gamarue bots

The command can be any of the following:

  • Download EXE (i.e., additional executable malware files)
  • Download DLL (i.e., additional malware; removed in version 2.09 and later)
  • Install plugin
  • Update bot (i.e., update the bot malware)
  • Delete DLLs (removed in version 2.09 and later)
  • Delete plugins
  • Kill bot

The last three commands can be used to remove evidence of Gamarue presence in machines.

The reply from the C&C server is also encrypted with RC4 algorithm using the same key used to encrypt the message from the infected machine.

Figure 8. Encrypted reply from C&C server

When decrypted, the reply contains the following information:

  • Time interval in minutes time to wait for when to ask the C2 server for the next command
  • Task ID – used by the hacker to track if there was an error performing the task
  • Command one of the command mentioned above
  • Download URL – from which a plugin/updated binary/other malware can be downloaded depending on the command.

Figure 9. Decrypted reply from C&C server

Anti-sandbox techniques

Gamarue employs anti-AV techniques to make analysis and detection difficult. Prior to infecting a machine, Gamarue checks a list hashes of the processes running on a potential victims machine. If it finds a process that may be associated with malware analysis tools, such as virtual machines or sandbox tools, Gamarue does not infect the machine. In older versions, a fake payload is manifested when running in a virtual machine.

Figure 10. Gamarue checks if any of the running processes are associated with malware analysis tools

Stealth mechanisms

Gamarue uses cross-process injection techniques to stay under the radar. It injects its code into the following legitimate processes:

  • msiexec.exe (Gamarue versions 2.07 to 2.10)
  • wuauclt.exe, wupgrade.exe, svchost.exe (version 2.06)

It can also use a rootkit plugin to hide the Gamarue file and its autostart registry entry.

Gamarue employs a stealthy technique to store and load its plugins as well. The plugins are stored fileless, either saved in the registry or in an alternate data stream of the Gamarue file.

OS tampering

Gamarue attempts to tamper with the operating systems of infected computers by disabling Firewall, Windows Update, and User Account Control functions. These functionalities cannot be re-enabled until the Gamarue infection has been removed from the infected machine. This OS tampering behavior does not work on Windows 10

Figure 11. Disabled Firewall and Windows Update

Monetization

There are several ways hackers earn using Gamarue. Since Gamarues main purpose is to distribute other malware, hackers earn using pay-per-install scheme. Using its plugins, Gamarue can also steal user information; stolen information can be sold to other hackers in cybercriminal underground markets. Access to Gamarue-infected machines can also be sold, rented, leased, or swapped by one criminal group to another.

Remediation

To help prevent a Gamarue infection, as well as other malware and unwanted software, take these precautions:

  • Be cautious when opening emails or social media messages from unknown users.
  • Be wary about downloading software from websites other than the program developers.

More importantly, ensure you have the right security solutions that can protect your machine from Gamarue and other threats. Windows Defender Antivirus detects and removes the Gamarue malware. With advanced machine learning models, as well as generic and heuristic techniques, Windows Defender AV detects new as well as never-before-seen malware in real-time via the cloud protection service. Alternatively, standalone tools, such as Microsoft Safety Scanner and the Malicious Software Removal Tool (MSRT), can also detect and remove Gamarue.

Microsoft Edge can block Gamarue infections from the web, such as those from malicious links in social media messages and drive-by downloads or exploit kits. Microsoft Edge is a secure browser that opens pages within low privilege app containers and uses reputation-based blocking of malicious downloads.

In enterprise environments, additional layers of protection are available. Windows Defender Advanced Threat Protection can help security operations personnel to detect Gamarue activities, including cross-process injection techniques, in the network so they can investigate and respond to attacks. Windows Defender ATPs enhanced behavioral and machine learning detection libraries flag malicious behavior across the malware infection process, from delivery and installation, to persistence mechanisms, and command-and-control communication.

Microsoft Exchange Online Protection (EOP) can block Gamarue infections from email uses built-in anti-spam filtering capabilities that help protect Office 365 customers. Office 365 Advanced Threat Protection helps secure mailboxes against email attacks by blocking emails with unsafe attachments, malicious links, and linked-to files leveraging time-of-click protection.

Windows Defender Exploit Guard can block malicious documents (such as those that distribute Gamarue) and scripts. The Attack Surface Reduction (ASR) feature in Windows Defender Exploit Guard uses a set of built-in intelligence that can block malicious behaviors observed in malicious documents. ASR rules can also be turned on to block malicious attachments from being run or launched from Microsoft Outlook or webmail (such as Gmail, Hotmail, or Yahoo).

Microsoft is also continuing the collaborative effort to help clean Gamarue-infected computers by providing a one-time package with samples (through the Virus Information Alliance) to help organizations protect their customers.

 

 

Microsoft Digital Crimes Unit and Windows Defender Research team

 

 

Get more info on the Gamarue (Andromeda) takedown from the following sources:

 

 


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Windows Defender ATP machine learning and AMSI: Unearthing script-based attacks that ‘live off the land’

December 4th, 2017 No comments

Data center

Scripts are becoming the weapon of choice of sophisticated activity groups responsible for targeted attacks as well as malware authors who indiscriminately deploy commodity threats.

Scripting engines such as JavaScript, VBScript, and PowerShell offer tremendous benefits to attackers. They run through legitimate processes and are perfect tools for living off the landstaying away from the disk and using common tools to run code directly in memory. Often part of the operating system, scripting engines can evaluate and execute content from the internet on-the-fly. Furthermore, integration with popular apps make them effective vehicles for delivering malicious implants through social engineering as evidenced by the increasing use of scripts in spam campaigns.

Malicious scripts are not only used as delivery mechanisms. We see them in various stages of the kill chain, including during lateral movement and while establishing persistence. During these latter stages, the scripting engine of choice is clearly PowerShellthe de facto scripting standard for administrative tasks on Windowswith the ability to invoke system APIs and access a variety of system classes and objects.

While the availability of powerful scripting engines makes scripts convenient tools, the dynamic nature of scripts allows attackers to easily evade analysis and detection by antimalware and similar endpoint protection products. Scripts are easily obfuscated and can be loaded on-demand from a remote site or a key in the registry, posing detection challenges that are far from trivial.

Windows 10 provides optics into script behavior through Antimalware Scan Interface (AMSI), a generic, open interface that enables Windows Defender Antivirus to look at script contents the same way script interpreters doin a form that is both unencrypted and unobfuscated. In Windows 10 Fall Creators Update, with knowledge from years analyzing script-based malware, weve added deep behavioral instrumentation to the Windows script interpreter itself, enabling it to capture system interactions originating from scripts. AMSI makes this detailed interaction information available to registered AMSI providers, such as Windows Defender Antivirus, enabling these providers to perform further inspection and vetting of runtime script execution content.

This unparalleled visibility into script behavior is capitalized further through other Windows 10 Fall Creators Update enhancements in both Windows Defender Antivirus and Windows Defender Advanced Threat Protection (Windows Defender ATP). Both solutions make use of powerful machine learning algorithms that process the improved optics, with Windows Defender Antivirus delivering enhanced blocking of malicious scripts pre-breach and Windows Defender ATP providing effective behavior-based alerting for malicious post-breach script activity.

In this blog, we explore how Windows Defender ATP, in particular, makes use of AMSI inspection data to surface complex and evasive script-based attacks. We look at advanced attacks perpetrated by the highly skilled KRYPTON activity group and explore how commodity malware like Kovter abuses PowerShell to leave little to no trace of malicious activity on disk. From there, we look at how Windows Defender ATP machine learning systems make use of enhanced insight about script characteristics and behaviors to deliver vastly improved detection capabilities.

KRYPTON: Highlighting the resilience of script-based attacks

Traditional approaches for detecting potential breaches are quite file-centric. Incident responders often triage autostart entries, sorting out suspicious files by prevalence or unusual name-folder combinations. With modern attacks moving closer towards being completely fileless, it is crucial to have additional sensors at relevant choke points.

Apart from not having files on disk, modern script-based attacks often store encrypted malicious content separately from the decryption key. In addition, the final key often undergoes multiple processes before it is used to decode the actual payload, making it is impossible to make a determination based on a single file without tracking the actual invocation of the script. Even a perfect script emulator would fail this task.

For example, the activity group KRYPTON has been observed hijacking or creating scheduled tasksthey often target system tasks found in exclusion lists of popular forensic tools like Autoruns for Windows. KRYPTON stores the unique decryption key within the parameters of the scheduled task, leaving the actual payload content encrypted.

To illustrate KRYPTON attacks, we look at a tainted Microsoft Word document identified by John Lambert and the Office 365 Advanced Threat Protection team.

KRYPTON lure document

Figure 1. KRYPTON lure document

To live off the land, KRYPTON doesnt drop or carry over any traditional malicious binaries that typically trigger antimalware alerts. Instead, the lure document contains macros and uses the Windows Scripting Host (wscript.exe) to execute a JavaScript payload. This script payload executes only with the right RC4 decryption key, which is, as expected, stored as an argument in a scheduled task. Because it can only be triggered with the correct key introduced in the right order, the script payload is resilient against automated sandbox detonations and even manual inspection.

KRYPTON script execution chain through wscript.exe

Figure 2. KRYPTON script execution chain through wscript.exe

Exposing actual script behavior with AMSI

AMSI overcomes KRYPTONs evasion mechanisms by capturing JavaScript API calls after they have been decrypted and ready to be executed by the script interpreter. The screenshot below shows part of the exposed content from the KRYPTON attack as captured by AMSI.

Part of the KRYPTON script payload captured by AMSI and sent to the cloud for analysis

Figure 3. Part of the KRYPTON script payload captured by AMSI and sent to the cloud for analysis

By checking the captured script behavior against indicators of attack (IoAs) built up by human experts as well as machine learning algorithms, Windows Defender ATP effortlessly flags the KRYPTON scripts as malicious. At the same time, Windows Defender ATP provides meaningful contextual information, including how the script is triggered by a malicious Word document.

Windows Defender ATP machine learning detection of KRYPTON script captured by AMSI

Figure 4. Windows Defender ATP machine learning detection of KRYPTON script captured by AMSI

PowerShell use by Kovter and other commodity malware

Not only advanced activity groups like KRYPTON are shifting from binary executables to evasive scripts. In the commodity space, Kovter malware uses several processes to eventually execute its malicious payload. This payload resides in a PowerShell script decoded by a JavaScript (executed by wscript.exe) and passed to powershell.exe as an environment variable.

Windows Defender ATP machine learning alert for the execution of the Kovter script-based payload

Figure 5. Windows Defender ATP machine learning alert for the execution of the Kovter script-based payload

By looking at the PowerShell payload content captured by AMSI, experienced analysts can easily spot similarities to PowerSploit, a publicly available set of penetration testing modules. While such attack techniques involve file-based components, they remain extremely hard to detect using traditional methods because malicious activities occur only in memory. Such behavior, however, is effortlessly detected by Windows Defender ATP using machine learning that combines detailed AMSI signals with signals generated by PowerShell activity in general.

Part of the Kovter script payload captured by AMSI and sent to the cloud for analysis

Figure 6. Part of the Kovter script payload captured by AMSI and sent to the cloud for analysis

Fresh machine learning insight with AMSI

While AMSI provides rich information from captured script content, the highly variant nature of malicious scripts continues to make them challenging targets for detection. To efficiently extract and identify new traits differentiating malicious scripts from benign ones, Windows Defender ATP employs advanced machine learning methods.

As outlined in our previous blog, we employ a supervised machine learning classifier to identify breach activity. We build training sets based on malicious behaviors observed in the wild and normal activities on typical machines, augmenting that with data from controlled detonations of malicious artifacts. The diagram below conceptually shows how we capture malicious behaviors in the form of process trees.

Process tree augmented by instrumentation for AMSI data

Figure 7. Process tree augmented by instrumentation for AMSI data

As shown in the process tree, the kill chain begins with a malicious document that causes Microsoft Word (winword.exe) to launch PowerShell (powershell.exe). In turn, PowerShell executes a heavily obfuscated script that drops and executes the malware fhjUQ72.tmp, which then obtains persistence by adding a run key to the registry. From the process tree, our machine learning systems can extract a variety of features to build expert classifiers for areas like registry modification and file creation, which are then converted into numeric scores that are used to decide whether to raise alerts.

With the instrumentation of AMSI signals added as part of the Windows 10 Fall Creators Update (version 1709), Windows Defender ATP machine learning algorithms can now make use of insight into the unobfuscated script content while continually referencing machine state changes associated with process activity. Weve also built a variety of script-based models that inspect the nature of executed scripts, such as the count of obfuscation layers, entropy, obfuscation features, ngrams, and specific API invocations, to name a few.

As AMSI peels off the obfuscation layers, Windows Defender ATP benefits from growing visibility and insight into API calls, variable names, and patterns in the general structure of malicious scripts. And while AMSI data helps improve human expert knowledge and their ability to train learning systems, our deep neural networks automatically learn features that are often hidden from human analysts.

Machine-learning detections of JavaScript and PowerShell scripts

Figure 8. Machine learning detections of JavaScript and PowerShell scripts

While these new script-based machine learning models augment our expert classifiers, we also correlate new results with other behavioral information. For example, Windows Defender ATP correlates the detection of suspicious script contents from AMSI with other proximate behaviors, such as network connections. This contextual information is provided to SecOps personnel, helping them respond to incidents efficiently.

Machine learning combines VBScript content from AMSI and tracked network activity

Figure 9. Machine learning combines VBScript content from AMSI and tracked network activity

Detection of AMSI bypass attempts

With AMSI providing powerful insight into malicious script activity, attacks are more likely to incorporate AMSI bypass mechanisms that we group into three categories:

  • Bypasses that are part of the script content and can be inspected and alerted on
  • Tampering with the AMSI sensor infrastructure, which might involve the replacement of system files or manipulation of the load order of relevant DLLs
  • Patching of AMSI instrumentation in memory

The Windows Defender ATP research team proactively develops anti-tampering mechanisms for all our sensors. We have devised heuristic alerts for possible manipulation of our optics, designing these alerts so that they are triggered in the cloud before the bypass can suppress them.

During actual attacks involving CVE-2017-8759, Windows Defender ATP not only detected malicious post-exploitation scripting activity but also detected attempts to bypass AMSI using code similar to one identified by Matt Graeber.

Windows Defender ATP alert based on AMSI bypass pattern

Figure 10. Windows Defender ATP alert based on AMSI bypass pattern

AMSI itself captured the following bypass code for analysis in the Windows Defender ATP cloud.

AMSI bypass code sent to the cloud for analysis

Figure 11. AMSI bypass code sent to the cloud for analysis

Conclusion: Windows Defender ATP machine learning and AMSI provide revolutionary defense against highly evasive script-based attacks

Provided as an open interface on Windows 10, Antimalware Scan Interface delivers powerful optics into malicious activity hidden in encrypted and obfuscated scripts that are oftentimes never written to disk. Such evasive use of scripts is becoming commonplace and is being employed by both highly skilled activity groups and authors of commodity malware.

AMSI captures malicious script behavior by looking at script content as it is interpreted, without having to check physical files or being hindered by obfuscation, encryption, or polymorphism. At the endpoint, AMSI benefits local scanners, providing the necessary optics so that even obfuscated and encrypted scripts can be inspected for malicious content. Windows Defender Antivirus, specifically, utilizes AMSI to dynamically inspect and block scripts responsible for dropping all kinds of malicious payloads, including ransomware and banking trojans.

With Windows 10 Fall Creators Update (1709), newly added script runtime instrumentation provides unparalleled visibility into script behaviors despite obfuscation. Windows Defender Antivirus uses this treasure trove of behavioral information about malicious scripts to deliver pre-breach protection at runtime. To deliver post-breach defense, Windows Defender ATP uses advanced machine learning systems to draw deeper insight from this data.

Apart from looking at specific activities and patterns of activities, new machine learning algorithms in Windows Defender ATP look at script obfuscation layers, API invocation patterns, and other features that can be used to efficiently identify malicious scripts heuristically. Windows Defender ATP also correlates script-based indicators with other proximate activities, so it can deliver even richer contextual information about suspected breaches.

To benefit from the new script runtime instrumentation and other powerful security enhancements like Windows Defender Exploit Guard, customers are encourage to install Windows 10 Fall Creators Update.

Read the The Total Economic Impact of Microsoft Windows Defender Advanced Threat Protection from Forrester to understand the significant cost savings and business benefits enabled by Windows Defender ATP. To directly experience how Windows Defender ATP can help your enterprise detect, investigate, and respond to advance attacks, sign up for a free trial.

 

Stefan Sellmer, Windows Defender ATP Research

with

Shay Kels, Windows Defender ATP Research

Karthik Selvaraj, Windows Defender Research

 

Additional readings

 


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