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Machine learning vs. social engineering

Machine learning is a key driver in the constant evolution of security technologies at Microsoft. Machine learning allows Microsoft 365 to scale next-gen protection capabilities and enhance cloud-based, real-time blocking of new and unknown threats. Just in the last few months, machine learning has helped us to protect hundreds of thousands of customers against ransomware, banking Trojan, and coin miner malware outbreaks.

But how does machine learning stack up against social engineering attacks?

Social engineering gives cybercriminals a way to get into systems and slip through defenses. Security investments, including the integration of advanced threat protection services in Windows, Office 365, and Enterprise Mobility + Security into Microsoft 365, have significantly raised the cost of attacks. The hardening of Windows 10 and Windows 10 in S mode, the advancement of browser security in Microsoft Edge, and the integrated stack of endpoint protection platform (EPP) and endpoint detection and response (EDR) capabilities in Windows Defender Advanced Threat Protection (Windows Defender ATP) further raise the bar in security. Attackers intent on overcoming these defenses to compromise devices are increasingly reliant on social engineering, banking on the susceptibility of users to open the gate to their devices.

Modern social engineering attacks use non-portable executable (PE) files like malicious scripts and macro-laced documents, typically in combination with social engineering lures. Every month, Windows Defender AV detects non-PE threats on over 10 million machines. These threats may be delivered as email attachments, through drive-by web downloads, removable drives, browser exploits, etc. The most common non-PE threat file types are JavaScript and VBScript.

Figure 1. Ten most prevalent non-PE threat file types encountered by Windows Defender AV

Non-PE threats are typically used as intermediary downloaders designed to deliver more dangerous executable malware payloads. Due to their flexibility, non-PE files are also used in various stages of the attack chain, including lateral movement and establishing fileless persistence. Machine learning allows us to scale protection against these threats in real-time, often protecting the first victim (patient zero).

Catching social engineering campaigns big and small

In mid-May, a small-scale, targeted spam campaign started distributing spear phishing emails that spoofed a landscaping business in Calgary, Canada. The attack was observed targeting less than 100 machines, mostly located in Canada. The spear phishing emails asked target victims to review an attached PDF document.

When opened, the PDF document presents itself as a secure document that requires action a very common social engineering technique used in enterprise phishing attacks. To view the supposed secure document, the target victim is instructed to click a link within the PDF, which opens a malicious website with a sign-in screen that asks for enterprise credentials.

Phished credentials can then be used for further attacks, including CEO fraud, additional spam campaigns, or remote access to the network for data theft or ransomware. Our machine learning blocked the PDF file as malware (Trojan:Script/Cloxer.A!cl) from the get-go, helping prevent the attack from succeeding.

Figure 2. Phishing email campaign with PDF attachment

Beyond targeted credential phishing attacks, we commonly see large-scale malware campaigns that use emails with archive attachments containing malicious VBScript or JavaScript files. These emails typically masquerade as an outstanding invoice, package delivery, or parking ticket, and instruct targets of the attack to refer to the attachment for more details. If the target opens the archive and runs the script, the malware typically downloads and runs further threats like ransomware or coin miners.

Figure 3. Typical social engineering email campaign with an archive attachment containing a malicious script

Malware campaigns like these, whether limited and targeted or large-scale and random, occur frequently. Attackers go to great lengths to avoid detection by heavily obfuscating code and modifying their attack code for each spam wave. Traditional methods of manually writing signatures identifying patterns in malware cannot effectively stop these attacks. The power of machine learning is that it is scalable and can be powerful enough to detect noisy, massive campaigns, but also specific enough to detect targeted attacks with very few signals. This flexibility means that we can stop a wide range of modern attacks automatically at the onset.

Machine learning models zero in on non-executable file types

To fight social engineering attacks, we build and train specialized machine learning models that are designed for specific file types.

Building high-quality specialized models requires good features for describing each file. For each file type, the full contents of hundreds of thousands of files are analyzed using large-scale distributed computing. Using machine learning, the best features that describe the content of each file type are selected. These features are deployed to the Windows Defender AV client to assist in describing the content of each file to machine learning models.

In addition to these ML-learned features, the models leverage expert researcher-created features and other useful file metadata to describe content. Because these ML models are trained for specific file types, they can zone in on the metadata of these file types.

Figure 4. Specialized file type-specific client ML models are paired with heavier cloud ML models to classify and protect against malicious script files in real-time

When the Windows Defender AV client encounters an unknown file, lightweight local ML models search for suspicious characteristics in the files features. Metadata for suspicious files are sent to the cloud protection service, where an array of bigger ML classifiers evaluate the file in real-time.

In both the client and the cloud, specialized file-type ML classifiers add to generic ML models to create multiple layers of classifiers that detect a wide range of malicious behavior. In the backend, deep-learning neural network models identify malicious scripts based on their full file content and behavior during detonation in a controlled sandbox. If a file is determined malicious, it is not allowed to run, preventing infection at the onset.

File type-specific ML classifiers are part of metadata-based ML models in the Windows Defender AV cloud protection service, which can make a verdict on suspicious files within a fraction of a second.

Figure 5. Layered machine learning models in Windows Defender ATP

File type-specific ML classifiers are also leveraged by ensemble models that learn and combine results from the whole array of cloud classifiers. This produces a comprehensive cloud-based machine learning stack that can protect against script-based attacks, including zero-day malware and highly targeted attacks. For example, the targeted phishing attack in mid-May was caught by a specialized PDF client-side machine learning model, as well as several cloud-based machine learning models, protecting customers in real-time.

Microsoft 365 threat protection powered by artificial intelligence and data sharing

Social engineering attacks that use non-portable executable (PE) threats are pervasive in todays threat landscape; the impact of combating these threats through machine learning is far-reaching.

Windows Defender AV combines local machine learning models, behavior-based detection algorithms, generics, and heuristics with a detonation system and powerful ML models in the cloud to provide real-time protection against polymorphic malware. Expert input from researchers, advanced technologies like Antimalware Scan Interface (AMSI), and rich intelligence from the Microsoft Intelligent Security Graph continue to enhance next-generation endpoint protection platform (EPP) capabilities in Windows Defender Advanced Threat Protection.

In addition to antivirus, components of Windows Defender ATPs interconnected security technologies defend against the multiple elements of social engineering attacks. Windows Defender SmartScreen in Microsoft Edge (also now available as a Google Chrome extension) blocks access to malicious URLs, such as those found in social engineering emails and documents. Network protection blocks malicious network communications, including those made by malicious scripts to download payloads. Attack surface reduction rules in Windows Defender Exploit Guard block Office-, script-, and email-based threats used in social engineering attacks. On the other hand, Windows Defender Application Control can block the installation of untrusted applications, including malware payloads of intermediary downloaders. These security solutions protect Windows 10 and Windows 10 in S mode from social engineering attacks.

Further, Windows Defender ATP endpoint detection and response (EDR) uses the power of machine learning and AMSI to unearth script-based attacks that live off the land. Windows Defender ATP allows security operations teams to detect and mitigate breaches and cyberattacks using advanced analytics and a rich detection library. With the April 2018 Update, automated investigation and advance hunting capabilities further enhance Windows Defender ATP. Sign up for a free trial.

Machine learning also powers Office 365 Advanced Threat Protection to detect non-PE attachments in social engineering spam campaigns that distribute malware or steal user credentials. This enhances the Office 365 ATP comprehensive and multi-layered solution to protect mailboxes, files, online storage, and applications against threats.

These and other technologies power Microsoft 365 threat protection to defend the modern workplace. In Windows 10 April 2018 Update, we enhanced signal sharing across advanced threat protection services in Windows, Office 365, and Enterprise Mobility + Security through the Microsoft Intelligent Security Graph. This integration enables these technologies to automatically update protection and detection and orchestrate remediation across Microsoft 365.

 

Gregory Ellison and Geoff McDonald
Windows Defender Research

 

 

 

 


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Enhancing Office 365 Advanced Threat Protection with detonation-based heuristics and machine learning

Email, coupled with reliable social engineering techniques, continues to be one of the primary entry points for credential phishing, targeted attacks, and commodity malware like ransomware and, increasingly in the last few months, cryptocurrency miners.

Office 365 Advanced Threat Protection (ATP) uses a comprehensive and multi-layered solution to protect mailboxes, files, online storage, and applications against a wide range of threats. Machine learning technologies, powered by expert input from security researchers, automated systems, and threat intelligence, enable us to build and scale defenses that protect customers against threats in real-time.

Modern email attacks combine sophisticated social engineering techniques with malicious links or non-portable executable (PE) attachments like HTML or document files to distribute malware or steal user credentials. Attackers use non-PE file formats because these can be easily modified, obfuscated, and made polymorphic. These file types allow attackers to constantly tweak email campaigns to try slipping past security defenses. Every month, Office 365 ATP blocks more than 500,000 email messages that use malicious HTML and document files that open a website with malicious content.

Figure 1. Typical email attack chain

Detonation-based heuristics and machine learning

Attackers employ several techniques to evade file-based detection of attachments and blocking of malicious URLs. These techniques include multiple redirections, large dynamic and obfuscated scripts, HTML for tag manipulation, and others.

Office 365 ATP protects customers from unknown email threats in real-time by using intelligent systems that inspect attachments and links for malicious content. These automated systems include a robust detonation platform, heuristics, and machine learning models.

Detonation in controlled environments exposes thousands of signals about a file, including behaviors like dropped and downloaded files, registry manipulation for persistence and storing stolen information, outbound network connections, etc. The volume of detonated threats translate to millions of signals that need to be inspected. To scale protection, we employ machine learning technologies to sort through this massive amount of information and determine a verdict for analyzed files.

Machine learning models examine detonation artifacts along with various signals from the following:

  • Static code analysis
  • File structure anomaly
  • Phish brand impersonation
  • Threat intelligence
  • Anomaly-based heuristic detections from security researchers

Figure 2. Classifying unknown threats using detonation, heuristics, and machine learning

Our machine learning models are trained to find malicious content using hundreds of thousands of samples. These models use raw signals as features with small modifications to allow for grouping signals even when they occur in slightly different contexts. To further enhance detection, some models are built using three-gram models that use raw signals sorted by timestamps recorded during detonation. The three-gram models tend to be more sparse than raw signals, but they can act as mini-signatures that can then be scored. These types of models fill in some of the gaps, resulting in better coverage, with little impact to false positives.

Machine learning can capture and expose even uncommon threat behavior by using several technologies and dynamic featurization. Features like image similarity matching, domain reputation, web content extraction, and others enable machine learning to effectively separate malicious or suspicious behavior from the benign.

Figure 3. Machine learning expands on traditional detection capabilities

Over time, as our systems automatically process and make a verdict on millions of threats, these machine learning models will continue to improve. In the succeeding sections, well describe some interesting malware and phishing campaigns detected recently by Office 365 ATP machine learning models.

Phishing campaigns: Online banking credentials

One of the most common types of phishing attacks use HTML and document files to steal online banking credentials. Gaining access to online bank accounts is one of the easiest ways that attackers can profit from illicit activities.

The email messages typically mimic official correspondence from banks. Phishers have become very good at crafting phishing emails. They can target global banks but also localize email content for local banks.
The HTML or document attachment are designed to look like legitimate sign-in pages or forms. Online banking credentials and other sensitive information entered into these files or websites are sent to attackers. Office 365s machine learning models detect this behavior, among other signals, to determine that such attachments are malicious and block offending email messages.

Figure 4. Sample HTML files that mimic online banking sign in pages. (Click to enlarge)

Phishing campaigns: Cloud storage accounts

Another popular example of phishing campaigns uses HTML or document attachments to steal cloud storage or email account details. The email messages imply that the recipient has received a document hosted in a cloud storage service. In order to supposedly open the said document, the recipient has to enter the cloud storage or email user name and password.

This type of phishing is very rampant because gaining access to either email or cloud storage opens a lot of opportunities for attackers to access sensitive documents or compromise the victims other accounts.

Figure 5. Sample HTML files that pose as cloud storage sign in pages. (Click to enlarge)

Tax-themed phishing and malware attacks

Tax-themed social engineering attacks circulate year-round as cybercriminals take advantage of the different country and region tax schedules. These campaigns use various messages related to tax filing to convincer users to click a link or open an attachment. The social engineering messages may say the recipient is eligible for tax refund, confirm that tax payment has been completed, or declare that payments are overdue, among others.

For example, one campaign intercepted by Office 365 ATP using machine learning implied that the recipient has not completed tax filing and is due for penalty. The campaign targeted taxpayers in Colombia, where tax filing ended in October. The email message aimed to alarm taxpayers by suggesting that they have not filed their taxes.

Figure 6. Tax-themed email campaign targeting taxpayers in Colombia. The subject line translates to: You have been fined for not filing your income tax returns

The attachment is a .rar file containing an HTML file. The HTML file contains the logo of Direccin de Impuestos y Aduanas Nacionales (DIAN), the Colombianes tax and customs organization, and a link to download a file.

Figure 7. Social engineering document with a malicious link

The link points to a shortened URL hxxps://bit[.]ly/2IuYkcv that redirects to hxxp://dianmuiscaingreso[.]com/css/sanci%C3%B3n%20declaracion%20de%20renta.doc, which downloads a malicious document.

Figure 8: Malicious URL information

The malicious document carries a downloader macro code. When opened, Microsoft Word issues a security warning. In the document are instructions to Enable content, which executes the embedded malicious VBA code.

Figure 9: Malicious document with malicious macro code

If the victim falls for this social engineering attack, the macro code downloads and executes a file from hxxp://dianmuiscaingreso.com/css/w.jpg. The downloaded executable file (despite the file name) is a file injector and password-stealing malware detected by Windows Defender AV as Trojan:Win32/Tiggre!rfn.

Because Office 365 ATP machine learning detects the malicious attachment and blocks the email, the rest of the attack chain is stopped, protecting customers at the onset.

Artificial intelligence in Office 365 ATP

As threats rapidly evolve and become increasingly complex, we continuously invest in expanding capabilities in Office 365 Advanced Threat Protection to secure mailboxes from attacks. Using artificial intelligence and machine learning, Office 365 ATP can constantly scale coverage for unknown and emerging threats in-real time.

Office 365 ATPs machine learning models leverage Microsofts wide network of threat intelligence, as well as seasoned threat experts who have deep understanding of malware, cyberattacks, and attacker motivation, to combat a wide range of attacks.

This enhanced protection from Office 365 ATP contributes to and enriches the integrated Microsoft 365 threat protection, which provides intelligent, integrated, and secure solution for the modern workplace. Microsoft 365 combines the benefits and security technologies of Office 365, Windows, and Enterprise Mobility Suite (EMS) platforms.

Office 365 ATP also shares threat signals to the Microsoft Intelligent Security Graph, which uses advanced analytics to link threat intelligence and security signals across Office 365, the Windows Defender ATP stack of defenses, and other sensors. For example, when a malicious file is detected by Office 365 ATP, that threat can also be blocked on endpoints protected by Windows Defender ATP and vice versa. Connecting security data and systems allows Microsoft security technologies like Office 365 ATP to continuously improve threat protection, detection, and response.

 

 

Office 365 Threat Research

Why Windows Defender Antivirus is the most deployed in the enterprise

Statistics about the success and sophistication of malware can be daunting. The following figure is no different: Approximately 96% of all malware is polymorphic meaning that it is only experienced by a single user and device before it is replaced with yet another malware variant. This is because in most cases malware is caught nearly as fast as its created, so malware creators continually evolve to try and stay ahead. Data like this hammer home how important it is to have security solutions in place that are as agile and innovative as the attacks.

The type of security solution needed has a complex job: It must protect users from hundreds of thousands of new threats every day and then it must learn and grow to stay ahead of the next wave of attacks. The solution cannot just react to the latest threats; it must be able to predict and prevent malware infections.

Over the last year, weve talked about how were investing in new innovations to address this challenging threat landscape, what weve delivered, and how it will change the dynamics. Today, I want to share the results of our new antivirus capabilities in Windows Defender Advanced Threat Protection (ATP) which are genuinely incredible because they will directly benefit the work you are doing.

Currently, our antivirus capabilities on Windows 10 are repeatedly earning top scores on independent tests, often outperforming the competition. This performance is the result of a complete redesign of our security solution.

Whats more, this same technology is available for our Windows 7 customers as well, so that they can remain secure during their transition to Windows 10.

It started back in 2015

Weve been working to make our antivirus capabilities increasingly more effective, and in 2015 our results in two major independent tests (AV-Comparatives and AV-TEST) began to improve dramatically. As you can see in the chart below, beginning in March 2015 our scores on AV-TEST began to rise rapidly, and, over the course of the next five months, we moved from scores averaging 85% on their Prevalence Test to (or near) 100%. Since then, weve maintained those types of scores consistently. Our scores on AV-Comparatives experienced a very similar spike, trajectory, and results.

In December 2017, we reached another milestone on AV-TEST, where we achieved a perfect score across both the Prevalence and Real-World based tests. Previously we had only scored a perfect 100% on one of the two tests for a given month. The following chart from the AV-TEST site shows our scores from November and December 2017 on Windows 7. These same scores are also applicable to Windows 10, which shares the same technology (and more).

For AV-Comparatives, we recently achieved another important quality milestone: For five consecutive months we detected all malware samples. Our previous best was four consecutive months. The AV-Comparatives chart below shows our February 2018 results where we scored a perfect 100% block rate.

While independent antivirus tests are one indicator of a security solutions capabilities and protections, its important to understand that this is only one part of a complete quality assessment.

For example, in the case of Windows Defender ATP (which integrates our antivirus capabilities and the whole Windows security stack), our customers have a much larger set of protection features none of which are factored into the tests. These features provide additional layers of protection that help prevent malware from getting onto devices in the first place. These features include the following:

If organizations like AV-Comparatives and AV-TEST performed complete security stack tests (i.e., testing against the complete endpoint protection solution) the results would often tell a very different story. For example, in November, we scored a 98.9% based on a single file miss on the Real-World test. The good news, however, is that we would have scored 100% if either Windows Defender Application Guard or Application Control was enabled.

How did we achieve these results?

The short answer is that we completely redesigned our antivirus solutions for both Windows 7 and Windows 10 from the ground up.

To do this, we moved away from using a static signature-based engine that couldnt scale due to its dependence on constant input from researchers. Weve now moved to a model that uses predictive technologies, machine learning, applied science, and artificial intelligence to detect and stop malware at first sight. We described the use of these technologies in our recent posts on Emotet and BadRabbit, as well as the recent Dofoil outbreak. These are the types of approaches that can be very successful against the ongoing avalanche of malware threats.

Because of these changes, our antivirus solution can now block malware using local and cloud-based machine learning models, combined with behavior, heuristic, and generic-based detections on the client. We can block nearly all of it at first sight and in milliseconds!

This is incredible.

Weve also designed our antivirus solution to work in both online and offline scenarios. When connected to the cloud, its fed real-time intelligence from the Intelligent Security Graph. For offline scenarios, the latest dynamic intelligence from the Graph is provisioned to the endpoint regularly throughout the day.

Weve also built our solution to defend against the new wave of fileless attacks, like Petya and WannaCry. To read more about how we protect against these attacks, check out the blog post Now you see me: Exposing fileless malware.

What this means to you

Each of these milestones is great, but the thing that makes us the most excited here at Microsoft is very simple: Customer adoption.

Right now, we are seeing big growth in enterprise environments our across all of our platforms:

  • 18% of Windows 7 and Windows 8 devices are using our antivirus solution
  • Over 50% of Windows 10 devices are using our antivirus solution

These are awesome numbers and proof that customers trust Windows security. What we are seeing is that as organizations are moving to Windows 10 they are also moving to our antivirus as their preferred solution. With our antivirus solution being used on more than 50% percent of the Windows 10 PCs deployed in commercial organizations, it is now the most commonly used antivirus solution in commercial organizations on that platform. This usage is in commercial customers of all sizes from small and medium-sized businesses to the largest enterprise organizations.

Over the past couple of months Ive shared this data with multiple customers, and often Im asked why weve seen such a positive increase. The answer is simple:

  1. Our antivirus capabilities are a fantastic solution! The test results above really speak for themselves. With five months of top scores that beat some of our biggest competitors, you can be confident that our solution can protect you from the most advanced threats.
  2. Our solution is both easier and operationally cheaper to maintain than others. Most enterprise customers use Config Manager for PC management of Windows 7 and Windows 10 security features, including antivirus. With Windows 10, the antivirus capabilities are built directly into the operating system and theres nothing to deploy. Windows 7 didnt include antivirus capabilities by default, but it can be deployed and configured in Config Manager. Now organizations do not have to maintain two infrastructures one for PC management and another for antivirus. Several years ago, our Microsoft IT department retired the separate global infrastructure that was used to manage Microsofts antivirus solution and now you can too! With our solution theres less to maintain and secure.
  3. Our solution enables IT to be more agile. On Windows 10 theres no agent security is built into the platform. When a new update of Windows 10 is released, you dont need to wait for a 3rd party to certify and support it; instead, you have full support and compatibility on day one. This means that new releases of Windows and all the latest security technologies can be deployed faster. This allows you to get current, stay current, and be more secure.
  4. Our solution offers a better user experience. Its designed to work behind the scenes in a way that is unobtrusive to end users and minimizes power consumption. This means longer battery life and everyone wants more battery life!

While weve made excellent progress with our antivirus solution, Im even more excited about the protection and management capabilities we will deliver to our customers in the near future. In the meantime, one of the best ways to evaluate our antivirus capabilities is when you run it with Windows Defender ATP. With Windows Defender ATP, the power of the Windows security stack provides preventative protection, detects attacks and zero-day exploits, and gives you centralized management for your end-to-end security lifecycle.

Sign up to try Windows Defender ATP for yourself!

 

How artificial intelligence stopped an Emotet outbreak

February 14th, 2018 No comments

At 12:46 a.m. local time on February 3, a Windows 7 Pro customer in North Carolina became the first would-be victim of a new malware attack campaign for Trojan:Win32/Emotet. In the next 30 minutes, the campaign tried to attack over a thousand potential victims, all of whom were instantly and automatically protected by Windows Defender AV.

How did Windows Defender AV uncover the newly launched attack and block it at the outset? Through layered machine learning, including use of both client-side and cloud machine learning (ML) models. Every day, artificial intelligence enables Windows Defender AV to stop countless malware outbreaks in their tracks. In this blog post, well take a detailed look at how the combination of client and cloud ML models detects new outbreaks.

Figure 1. Layered detected model in Windows Defender AV

Client machine learning models

The first layer of machine learning protection is an array of lightweight ML models built right into the Windows Defender AV client that runs locally on your computer. Many of these models are specialized for file types commonly abused by malware authors, including, JavaScript, Visual Basic Script, and Office macro. Some models target behavior detection, while other models are aimed at detecting portable executable (PE) files (.exe and .dll).

In the case of the Emotet outbreak on February 3, Windows Defender AV caught the attack using one of the PE gradient boosted tree ensemble models. This model classifies files based on a featurization of the assembly opcode sequence as the file is emulated, allowing the model to look at the files behavior as it was simulated to run.

Figure 2. A client ML model classified the Emotet outbreak as malicious based on emulated execution opcode machine learning model.

The tree ensemble was trained using LightGBM, a Microsoft open-source framework used for high-performance gradient boosting.

Figure 3a. Visualization of the LightBGM-trained client ML model that successfully classified Emotet’s emulation behavior as malicious. A set of 20 decision trees are combined in this model to classify whether the files emulated behavior sequence is malicious or not.

Figure 3b. A more detailed look at the first decision tree in the model. Each decision is based on the value of a different feature. Green triangles indicate weighted-clean decision result; red triangles indicate weighted malware decision result for the tree.

When the client-based machine learning model predicts a high probability of maliciousness, a rich set of feature vectors is then prepared to describe the content. These feature vectors include:

  • Behavior during emulation, such as API calls and executed code
  • Similarity fuzzy hashes
  • Vectors of content descriptive flags optimized for use in ML models
  • Researcher-driven attributes, such as packer technology used for obfuscation
  • File name
  • File size
  • Entropy level
  • File attributes, such as number of sections
  • Partial file hashes of the static and emulated content

This set of features form a signal sent to the Windows Defender AV cloud protection service, which runs a wide array of more complex models in real-time to instantly classify the signal as malicious or benign.

Real-time cloud machine learning models

Windows Defender AVs cloud-based real-time classifiers are powerful and complex ML models that use a lot of memory, disk space, and computational resources. They also incorporate global file information and Microsoft reputation as part of the Microsoft Intelligent Security Graph (ISG) to classify a signal. Relying on the cloud for these complex models has several benefits. First, it doesnt use your own computers precious resources. Second, the cloud allows us to take into consideration the global information and reputation information from ISG to make a better decision. Third, cloud-based models are harder for cybercriminals to evade. Attackers can take a local client and test our models without our knowledge all day long. To test our cloud-based defenses, however, attackers have to talk to our cloud service, which will allow us to react to them.

The cloud protection service is queried by Windows Defender AV clients billions of times every day to classify signals, resulting in millions of malware blocks per day, and translating to protection for hundreds of millions of customers. Today, the Windows Defender AV cloud protection service has around 30 powerful models that run in parallel. Some of these models incorporate millions of features each; most are updated daily to adapt to the quickly changing threat landscape. All together, these classifiers provide an array of classifications that provide valuable information about the content being scanned on your computer.

Classifications from cloud ML models are combined with ensemble ML classifiers, reputation-based rules, allow-list rules, and data in ISG to come up with a final decision on the signal. The cloud protection service then replies to the Windows Defender client with a decision on whether the signal is malicious or not all in a fraction of a second.

Figure 4. Windows Defender AV cloud protection service workflow.

In the Emotet outbreak, one of our cloud ML servers in North America received the most queries from customers; corresponding to where the outbreak began. At least nine real-time cloud-based ML classifiers correctly identified the file as malware. The cloud protection service replied to signals instructing the Windows Defender AV client to block the attack using two of our ML-based threat names, Trojan:Win32/Fuerboos.C!cl and Trojan:Win32/Fuery.A!cl.

This automated process protected customers from the Emotet outbreak in real-time. But Windows Defender AVs artificial intelligence didnt stop there.

Deep learning on the full file content

Automatic sample submission, a Windows Defender AV feature, sent a copy of the malware file to our backend systems less than a minute after the very first encounter. Deep learning ML models immediately analyzed the file based on the full file content and behavior observed during detonation. Not surprisingly, deep neural network models identified the file as a variant of Trojan:Win32/Emotet, a family of banking Trojans.

While the ML classifiers ensured that the malware was blocked at first sight, deep learning models helped associate the threat with the correct malware family. Customers who were protected from the attack can use this information to understand the impact the malware might have had if it were not stopped.

Additionally, deep learning models provide another layer of protection: in relatively rare cases where real-time classifiers are not able to come to a conclusive decision about a file, deep learning models can do so within minutes. For example, during the Bad Rabbit ransomware outbreak, Windows Defender AV protected customers from the new ransomware just 14 minutes after the very first encounter.

Intelligent real-time protection against modern threats

Machine learning and AI are at the forefront of the next-gen real-time protection delivered by Windows Defender AV. These technologies, backed by unparalleled optics into the threat landscape provided by ISG as well as world-class Windows Defender experts and researchers, allow Microsoft security products to quickly evolve and scale to defend against the full range of attack scenarios.

Cloud-delivered protection is enabled in Windows Defender AV by default. To check that its running, go to Windows Settings > Update & Security > Windows Defender. Click Open Windows Defender Security Center, then navigate to Virus & threat protection > Virus &threat protection settings, and make sure that Cloud-delivered protection and Automatic sample submission are both turned On.

In enterprise environments, the Windows Defender AV cloud protection service can be managed using Group Policy, System Center Configuration Manager, PowerShell cmdlets, Windows Management Instruction (WMI), Microsoft Intune, or via the Windows Defender Security Center app.

The intelligent real-time defense in Windows Defender AV is part of the next-gen security technologies in Windows 10 that protect against a wide spectrum of threats. Of particular note, Windows 10 S is not affected by this type of malware attack. Threats like Emotet wont run on Windows 10 S because it exclusively runs apps from the Microsoft Store. Learn more about Windows 10 S. To know about all the security technologies available in Windows 10, read Microsoft 365 security and management features available in Windows 10 Fall Creators Update.

 

Geoff McDonald, Windows Defender Research
with Randy Treit and Allan Sepillo

 

 


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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.