Create custom reports using Microsoft Defender ATP APIs and Power BI
Typical enterprise security operation teams often rely on dependable reporting visualisations to make critical security decisions. While Microsoft Defender ATP provides extensive visibility on the security posture of your organization through built-in dashboards, custom reporting can help you turn security data from multiple sources into insights to meet your analytical needs.Hunting for reconnaissance activities using LDAP search filters
Attackers are known to use LDAP to gather information about users, machines, and the domain structure. Attackers can then take over high-privileged accounts by finding the shortest path to sensitive assets. Spotting these reconnaissance activities, especially from patient zero machines, is critical in detecting and containing cyberattacks.Getting Started with Windows Defender ATP Advanced Hunting
We’ve recently released a capability called Advanced Hunting in Windows Defender ATP that allows you to get unfiltered access to the raw data inside your Windows Defender ATP tenant and proactively hunt for threats using a powerful search and query language. Why should I care about Advanced Hunting? There will be situations where you need to quickly determine if your organization is impacted by a threat that does not yet have pre-established indicators of compromise (IOC). Think of a new global outbreak, or a new waterhole technique which could have lured some of your end users, or a new 0-day exploit. Imagine the following scenario. It’s early morning and you just got to the office. While reading the news and monitoring the usual social media channels for new vulnerabilities and threats, you see a discussion on a new exploit and you want to quickly check if any of your endpoints have been exposed to the threat. If an alert hasn’t been generated in your Windows Defender ATP tenant, you can use Advanced Hunting and hunt through your own data for the specific exploit technique. Based on the results of your query, you’ll quickly be able to see relevant information and take swift action where needed. How does Advanced Hunting work under the hood? Advanced Hunting makes use of the Azure Kusto query language, which is the same language we use for Azure Log Analytics, and provides full access to raw data up to 30 days back. The data model is simply made up by 10 tables in total, and all of the details on the fields of each table is available under our documentation, Advanced hunting reference in Windows Defender ATP. Alert information: This table includes information related to alerts and related IOCs Machine info: Includes properties of the devices (Name, OS platform and version, LoggedOn users, and others) Machine network info (Preview): The device network interfaces related information Process creation event: The process image file information, command line, and others Load image: The process and loaded module information Network communication events: The process and connection information File creation events: The created file info Registry activities: Which process change what key and which value LogOn event: Who logged on, type of logon, permissions, and others Events: A variety of Windows related events, for example telemetry from Windows Defender Exploit Guard You can easily combine tables in your query or search across any available table combination of your own choice. Whatever is needed for you to hunt! How do I write my first query? The easiest way I found to teach someone Advanced Hunting is by comparing this capability with an Excel spreadsheet that you can pivot and apply filters on. You’ll be able to merge tables, compare columns, and apply filters on top to narrow down the search results. Advanced Hunting uses simple query language but powerful query language that returns a rich set of data. Because of the richness of data, you will want to use filters wisely to reduce unnecessary noise into your analysis. The query language has plenty of useful operators, like the one that allows you to return up only a specific number of rows, which is useful to have for scenarios when you need a quick, performant, and focused set of results. Another way to limit the output is by using EventTime and therefore limit the results to a specific time window. For more information, see Advanced Hunting query best practices. Let’s take a closer look at this and get started. In our first example, we’ll use a table called ProcessCreationEvents and see what we can learn from there. First let’s look at the last 5 rows of ProcessCreationEvents and then let’s see what happens if instead of using the operator limit we use EventTime and filter for events that happened within the last hour. ProcessCreationEvents | limit 5 Image 1: Example query that returns random 5 rows of ProcessCreationEvents table, to quickly see some data ProcessCreationEvents | where EventTime > ago(1h) Image 2: Example query that returns all events from ProcessCreationEvents table that happened within the last hour Image 3: Outcome of ProcessCreationEvents with EventTime restriction Now remember earlier I compared this with an Excel spreadsheet. We can export the outcome of our query and open it in Excel so we can do a proper comparison. As you can see in the following image, all the rows that I mentioned earlier are displayed. Image 4: Exported outcome of ProcessCreationEvents with EventTime restriction which is started in Excel As with any other Excel sheet, all you really need to understand is where, and how, to apply filters, to get the information you’re looking for. When you master it, you will master Advanced Hunting! With that in mind, it’s time to learn a couple of more operators and make use of them inside a query. How do I filter for specific activities? There are several ways to apply filters for specific data. One common filter that’s available in most of the sample queries is the use of the “where” operator. This operator allows you to apply filters to a specific column within a table. For example, if you want to search for ProcessCreationEvents, where the FileName is powershell.exe. all you need to do is apply the operator in the following query: ProcessCreationEvents | where FileName == "powershell.exe" Image 5: Example query that shows all ProcessCreationEvents where the FileName is powershell.exe But remember you’ll want to either use the limit operator or the EventTime row as a filter to have the best results when running your query. Also note that sometimes you might not have the absolute filename or might be dealing with a malicious file that constantly changes names. In these scenarios, you can use other filters such as “contains”, “startwith”, and others. Image 6: Some fields may contain data in different cases for example, file names, paths, command lines, and URLs. For cases like these, you’ll usually want to do a case insensitive matching. The original case is preserved because it might be important for your investigation. ProcessCreationEvents | where FileName =~ "powershell.exe" | limit 5 Image 7: Example query that returns the last 5 rows of ProcessCreationEvents where FileName was powershell.exe. We are using =~ making sure it is case-insensitive. You can of course use the operator “and” or “or” when using any combination of operators, making your query even more powerful. ProcessCreationEvents | where FileName =~ "powershell.exe" or FileName =~ "cmd.exe" | limit 5 Image 8: Example query that returns the last 5 rows of ProcessCreationEvents where FileName was powershell.exe or cmd.exe In some instances, you might want to search for specific information across multiple tables. For that scenario, you can use the “find” operator. Think of the scenario where you are aware of a specific malicious file hash and you want to know details of that file hash across FileCreationEvents, ProcessCreationEvents, and NetworkCommunicatonEvents. let fileHash = "e152f7ce2d3a4349ac583580c2caf8f72fac16ba"; find in (FileCreationEvents, ProcessCreationEvents, NetworkCommunicationEvents) where SHA1 == fileHash or InitiatingProcessSHA1 == fileHash project ComputerName, ActionType, FileName, EventTime Image 9: Example query that searches for a specific file hash across multiple tables where the SHA1 equals to the file hash How do I tailor the outcome of a query? At some point, you may want to tailor the outcome of a query after running it so that you can see the most relevant information as quickly as possible. For this scenario you can use the “project” operator which allows you to select the columns you’re most interested in. Simply select which columns you want to visualize. ProcessCreationEvents | where FileName == "powershell.exe" or FileName == "cmd.exe" | limit 5 | project EventTime, ComputerName, ProcessCommandLine Image 10: Example query that returns the last 5 rows of ProcessCreationEvents where FileName was powershell.exe or cmd.exe, note this time we are using == which makes it case sensitive and where the outcome is filtered to show you EventTime, ComputerName and ProcessCommandLine Image 11: Result of the previous query There may be scenarios when you want to keep track of how many times a specific event happened on an endpoint. You can use the “summarize” operator for that, which allows you to produce a table that aggregates the content of the input table in combination with count() that will count the number of rows or dcount() that will count the distinct values. ProcessCreationEvents | where FileName == "powershell.exe" | summarize count() Image 12: Example query that searches for all ProcessCreationEvents where FileName was powershell.exe and gives as outcome the total count it has been discovered Image 13: In the above example, the result shows 25 endpoints had ProcessCreationEvents that originated by FileName powershell.exe ProcessCreationEvents | where FileName == "powershell.exe" | summarize dcount(ComputerName) Image 14: Query that searches for all ProcessCreationEvents where FileName was powershell.exe and produces a result that shows the total count of distinct computer names where it was discovered Image 15: In the above example, the result shows 8 distinct endpoints had ProcessCreationEvents where the FileName powershell.exe was seen You might have noticed a filter icon within the Advanced Hunting console. This is a useful feature to further optimize your query by adding additional filters based on the current outcome of your existing query. Image 16: select the filter option to further optimize your query Image 17: Depending on the current outcome of your query the filter will show you the available filters. Choosing the minus icon will exclude a certain attribute from the query while the addition icon will include it. Once you select any additional filters Run query turns blue and you will be able to run an updated query. How do I join multiple tables in one query? It almost feels like that there is an operator for anything you might want to do inside Advanced Hunting. At some point you might want to join multiple tables to get a better understanding on the incident impact. For that scenario, you can use the “join” operator. FileCreationEvents | where EventTime > ago(30d) | project MachineId, FileName, SHA1, FolderPath | join kind= inner ( ProcessCreationEvents | where EventTime > ago(30d) | project MachineId, FileName, SHA1, FolderPath ) on MachineId, SHA1, FolderPath, FileName | limit 10 Image 18: Example query that joins FileCreationEvents with ProcessCreationEvents where the result shows a full perspective on the files that got created and executed This is particularly useful for instances where you want to hunt for occurrences where threat actors drop their payload and run it afterwards. This way you can correlate the data and don’t have to write and run two different queries. Breakdown of some of the sample queries PowerShell execution events that could involve downloads This sample query searches for PowerShell activities that could indicate that the threat actor downloaded something from the network. Let’s break down the query to better understand how and why it is built in this way. ProcessCreationEvents | where EventTime > ago(7d) | where FileName in~ ("powershell.exe", "powershell_ise.exe") | where ProcessCommandLine has "Net.WebClient" or ProcessCommandLine has "DownloadFile" or ProcessCommandLine has "Invoke-WebRequest" or ProcessCommandLine has "Invoke-Shellcode" or ProcessCommandLine contains "http:" | project EventTime, ComputerName, InitiatingProcessFileName, FileName, ProcessCommandLine | top 100 by EventTime Image 19: PowerShell execution events that could involve downloads sample query ProcessCreationEvents Table selected | where EventTime > ago(7d) Only looking for events happened last 7 days | where FileName in~ (“powershell.exe”, “powershell_ise.exe”) Only looking for events where FileName is any of the mentioned PowerShell variations. Note because we use in ~ it is case-insensitive. | where ProcessCommandLine has "Net.WebClient" or ProcessCommandLine has "DownloadFile" or ProcessCommandLine has "Invoke-WebRequest" or ProcessCommandLine has "Invoke-Shellcode" or ProcessCommandLine has "http:" Only looking for PowerShell events where the used command line is any of the mentioned ones in the query | project EventTime, ComputerName, InitiatingProcessFileName, FileName, ProcessCommandLine Makes sure the outcome only shows EventTime, ComputerName, InitiatingProcessFileName, FileName and ProcessComandLine | top 100 by EventTime Ensures that the records are ordered by the top 100 of the EventTime Identifying Base64 decoded payload execution It has become very common for threat actors to do a Base64 decoding on their malicious payload to hide their traps. ProcessCreationEvents | where EventTime > ago(14d) | where ProcessCommandLine contains ".decode('base64')" or ProcessCommandLine contains "base64 --decode" or ProcessCommandLine contains ".decode64(" | project EventTime , ComputerName , FileName , FolderPath , ProcessCommandLine , InitiatingProcessCommandLine | top 100 by EventTime Image 20: Identifying Base64 decoded payload execution ProcessCreationEvents Table selected | where EventTime > ago(14d) Only looking for events happened last 14 days | where ProcessCommandLine contains ".decode('base64')" or ProcessCommandLine contains "base64 --decode" or ProcessCommandLine contains ".decode64(" Only looking for events where the command line contains an indication for base64 decoding. -> .decode('base64') -> base64 –decode -> .decode64( | project EventTime , ComputerName , FileName , FolderPath , ProcessCommandLine , InitiatingProcessCommandLine Make sure that the outcome only shows EventTime , ComputerName , FileName , FolderPath , ProcessCommandLine , InitiatingProcessCommandLine | top 100 by EventTime Ensures that the records are ordered by the top 100 of the EventTime Identifying network connections to known Dofoil NameCoin servers Dofoil is a sophisticated threat that attempted to install coin miner malware on hundreds of thousands of computers in March, 2018. The sample query below allows you to quickly determine if there’s been any network connections to known Dofoil NameCoin servers within the last 30 days from endpoints in your network. NetworkCommunicationEvents | where RemoteIP in ("139.59.208.246","130.255.73.90","31.3.135.232","52.174.55.168","185.121.177.177","185.121.177.53", "62.113.203.55","144.76.133.38","169.239.202.202","5.135.183.146","142.0.68.13","103.253.12.18", "62.112.8.85","69.164.196.21","107.150.40.234","162.211.64.20","217.12.210.54","89.18.27.34","193.183.98.154","51.255.167.0","91.121.155.13","87.98.175.85","185.97.7.7") | project ComputerName, InitiatingProcessCreationTime, InitiatingProcessFileName, InitiatingProcessCommandLine, RemoteIP, RemotePort Image 21: Identifying network connections to known Dofoil NameCoin servers NetworkCommunicationEvents Table selected | where RemoteIP in ("139.59.208.246","130.255.73.90","31.3.135.232", "52.174.55.168", "185.121.177.177","185.121.177.53","62.113.203.55", "144.76.133.38","169.239.202.202","5.135.183.146", "142.0.68.13","103.253.12.18","62.112.8.85", "69.164.196.21" ,"107.150.40.234","162.211.64.20","217.12.210.54" ,"89.18.27.34","193.183.98.154","51.255.167.0" ,"91.121.155.13","87.98.175.85","185.97.7.7") Only looking for network connection where the RemoteIP is any of the mentioned ones in the query | project ComputerName, InitiatingProcessCreationTime, InitiatingProcessFileName, InitiatingProcessCommandLine, RemoteIP, RemotePort Makes sure the outcome only shows ComputerName, InitiatingProcessCreationTime, InitiatingProcessFileName, InitiatingProcessCommandLine, RemoteIP, RemotePort Knowledge Check In the following sections, you’ll find a couple of queries that need to be fixed before they can work. Try to find the problem and address it so that the query can work. Don’t worry, there are some hints along the way. NetworkCommunicationEvents | where RemoteIP in (“msupdater.com”, “twitterdocs.com”) | summarize by ComputerName, InitiatingProcessComandLine 22: This query should return a result that shows network communication to two URLs msupdater.com and twitterdocs.com Image 23: This query should return a result that shows files downloaded through Microsoft Edge and returns the columns EventTime, ComputerName, InitiatingProcessFileName, FileName and FolderPath How do I manage queries? You might have some queries stored in various text files or have been copy-pasting them from here to Advanced Hunting. Advanced Hunting allows you to save your queries and share them within your tenant with your peers. Image 24: You can choose Save or Save As to select a folder location Image 25: Choose if you want the query to be shared across your organization or only available to you Summary We regularly publish new sample queries on GitHub. I highly recommend everyone to check these queries regularly. See, Sample queries for Advanced hunting in Windows Defender ATP. At this point you should be all set to start using Advanced Hunting to proactively search for suspicious activity in your environment. If you have questions, feel free to reach me on my Twitter handle: @MiladMSFT.
Hunting tip of the month: Browser downloads
Downloads from browsers are often used to initiate cyberattacks. Targeted attacks may use watering holes or spear-phishing messages with links, while commodity threats often originate from malicious ad campaigns or are downloaded by software bundlers. With this being one of the most common entry points for malware, you might be excited to know that Windows Defender Advanced Threat Protection (Windows Defender ATP) tracks the origin of most files that are downloaded by Microsoft Edge or Google Chrome, and that you could use this information on Advanced hunting to search for abnormal activities or pivot to related machines. Explaining the data: Where is it from and what does it mean? In most cases, FileOriginUrl and FileOriginIP are the URLs and IPs from which the file was downloaded, while FileOriginReferrerUrl is the URL that referred or linked to the download URL. This referrer URL is often the URL of a site page or a webmail page with a download link. Alternatively, this referrer URL might just be the previous link in a long chain of redirects used to proliferate malware. Our main source for this information is the Zone.Identifier alternate data stream, that is used by multiple applications to set the “mark of the web” of downloaded files. Starting from Windows 10 version 1703, the Windows Defender ATP sensor collects this data for all the reported files. In Advanced hunting, we recently exposed a few fields that we parse from the stream—our FileOriginUrl column is parsed from the HostUrl field, FileOriginIP is parsed from HostIpAddress, and FileOriginReferrerUrl is parsed from ReferrerUrl. Multiple Windows applications currently set a file’s origin details in that stream. Microsoft has enabled Edge to set this information, so do Office programs, unzipping events, and more. Google has updated the Chrome browser accordingly, and so have multiple other products such as WinRar. When explorer.exe copies files with this information, it sets this information for the new copies as well. All application developers can set this data for files that they create—see this Firefox bug for more details. It is, however, important to note that any program could set these fields in the Zone.Identifier alternate data stream, so the usage and actual meaning of values may vary. For example, during file extraction, many file compression apps use the path of the archive as the FileOriginReferrerUrl of the extracted files. As a result, this field will contain a local path instead of the expected web page URL. In some cases, we could get the file origin information from other sources, but would still upload it to use the above columns. Specifically, in Windows Defender Antivirus events, the file origin details are received from the IOfficeAntiVirus API, which is used by browsers, Microsoft Office, file-sharing apps, and many other apps to request the enabled AV to scan downloaded files. In these events, FileOriginUrl is set with the URL from which the detected file was downloaded. NOTE: All data that is available through Advanced hunting is from your tenant. You can find more information on data privacy here. Example 1: Tracing footprints—find all files downloaded from a certain site If you see a suspicious download, you may want to see which other files were downloaded from that URL or site. In this example, Windows Defender ATP raised an alert for malicious activity. As you can see in the process tree and its side-pane, a file was downloaded from a site called napptayiyal[.]com. By going to the URL page for napptayiyal[.]com in Windows Defender ATP, you can see that this site was recently registered through a privacy protection service and is hosted on AWS. The URL page will show you all downloads from that site. However, let’s run a similar simple query in Advanced hunting, where we can continue to tweak the query to filter noise or expand our search as shown in the next few examples. To see what fields parse_url() can extract, read this documentation. parse_url() returns a dynamic object, so in order to apply an endswith condition on it, we need to first convert it to a string using tostring(). Example 2: Hunt for payloads hosted as user content in Dropbox Over the years, we have observed many malicious payloads being downloaded from popular online services like GitHub, Dropbox, Google Drive, OneDrive, Azure blob storage, and AWS storage. All these cloud vendors, including Microsoft, are committed to blocking and removing malicious payloads, but these services continue to be infection vectors. It is often easier for attackers to host files on public cloud infrastructure than to set up their own infrastructure. The good reputation and popularity of these services also make content they serve generally difficult for security providers and defenders to regulate. In this example, we focus on downloads from Dropbox, such as the one described in this post by FireEye. Dropbox hosts user content under dl.dropboxusercontent.com. As you can see in this query, most downloads have the referrer page within www.dropbox.com. For malware, however, this is usually not the case—attackers want the download to be seamless, without users seeing that dropbox.com is even involved. DeviceFileEvents | where FileOriginUrl startswith https://dl.dropboxusercontent.com/ | extend ReferrerHost=tostring(parse_url(FileOriginReferrerUrl).Host) | summarize count(), any(FileName) by ReferrerHost After filtering out downloads that start from www.dropbox.com we are left with little noise. Feel free to access the full query, including this extra filter, on GitHub. Example 3: Pivot from Windows Defender AV detections Windows Defender Antivirus scans every file downloaded through the browser and reports both the details of the file that was detected and the URL it was downloaded from. In this query, we pivot from the download URLs of detected files to other downloads from the same hosts, specifically the downloads that were not detected. In this example we explicitly filter out threat categories that are less severe, such as software bundlers or Potentially Unwanted Applications (PUA), so we could hunt for the more interesting stuff. let detectedDownloads = DeviceEvents | where ActionType == "AntivirusDetection" and isnotempty(FileOriginUrl) | project Timestamp, FileOriginUrl, FileName, DeviceId, ThreatName=tostring(parse_json(AdditionalFields).ThreatName) // Filter out less severe threat categories on which we do not want to pivot | where ThreatName !startswith "PUA" and ThreatName !startswith "SoftwareBundler:" and FileOriginUrl != "about:internet"; let detectedDownloadsSummary = detectedDownloads // Get a few examples for each detected Host: // up to 4 filenames, up to 4 threat names, one full URL) | summarize DetectedUrl=any(FileOriginUrl), DetectedFiles=makeset(FileName, 4), ThreatNames=makeset(ThreatName, 4) by Host=tostring(parse_url(FileOriginUrl).Host); // Query for downloads from sites from which other downloads were detected by Windows Defender Antivirus DeviceFileEvents | where isnotempty(FileOriginUrl) | project FileName, FileOriginUrl, DeviceId, Timestamp, Host=tostring(parse_url(FileOriginUrl).Host), SHA1 // Filter downloads from hosts serving detected files | join kind=inner(detectedDownloadsSummary) on Host // Filter out download file create events that were also detected. // This is needed because sometimes both of these events will be reported, // and sometimes only the AntivirusDetection event - depending on timing. | join kind=leftanti(detectedDownloads) on DeviceId, FileOriginUrl // Summarize a single row per host - with the machines count // and an example event for a missed download (select the last event) | summarize MachineCount=dcount(DeviceId), arg_max(Timestamp, *) by Host // Filter out common hosts, as they probably ones that also serve benign files | where MachineCount < 20 | project Host, MachineCount, DeviceId, FileName, DetectedFiles, FileOriginUrl, DetectedUrl, ThreatNames, Timestamp, SHA1 | order by MachineCount desc Famous last words I apologize if this post was a bit long, but I hope you have found it interesting. You are more than welcome to visit the GitHub repository to find other interesting queries or contribute your own ideas. If all these seem new to you, experience Windows Defender ATP for free now. Also, perhaps you would be interested in other posts on Advanced hunting. The next hunting tip demoes hunting on top of downloads that originate from email links. You may also like this entry-level tutorial for Advanced hunting or an in-depth look into hunting on Powershell commands. I really appreciate you reading this far. Honestly, mom, this means a lot to me. 😉Multi-layer defense against attacks that abuse the .settingcontent-ms file type
Attackers are always on the lookout for new ways to infiltrate systems and networks. Not long after security researcher Matt Nelson detailed a way to abuse the .settingcontent-ms file format to load shell commands, cybercriminals experimented with ways to use this technique in attacks. Microsoft 365 has multiple layers of defense against this new attacker technique. These protections include mitigations in Office 365, as well as detection and investigation capabilities in the Windows Defender Advanced Threat Protection (Windows Defender ATP) unified security platform. Office 365 By default, Office 365 applications will block .settingcontent-ms objects inside documents. This is meant to defeat social engineering attacks that embed malicious executables or scripts (for example, .exe, .js, .vbs files) in Office documents. Outlook uses the same list of blocked file extensions to block attachments. Figure 1. Office 365 apps will not allow the activation of objects that link to file name extensions considered high risk Attack surface reduction Attack surface reduction capabilities in Windows Defender ATP provide a set of built-in intelligence that can block underlying behaviors used by malicious documents. The following Attack surface reduction rules, updated immediately after the attacker technique was made public, protect against attacks that use documents with .settingcontent-ms objects: Block Office applications from creating child processes (D4F940AB-401B-4EFC-AADC-AD5F3C50688A) Block Office applications from creating executable content (3B576869-A4EC-4529-8536-B80A7769E899) In addition, the following rule, available in preview, protects against malicious .pdf files with embedded .settingcontent-ms objects: Block Adobe Reader from creating child processes (7674ba52-37eb-4a4f-a9a1-f0f9a1619a2c) The following rule, also in preview, protects against emails that have .settingcontent-ms objects embedded: Block Office communication applications from creating child processes (26190899-1602-49e8-8b27-eb1d0a1ce869) Figure 2. Attack surface reduction rules prevent execution of abused .settingconten-ms files Next Generation Protection Antivirus capabilities in Windows Defender ATP detect and block malicious .settingcontent-ms files as Trojan:O97M/DPlink.A. Figure 3. Alert raised in Windows Defender Security Center for Trojan:O97M/DPlink.A Endpoint detection and response Endpoint detection and response capabilities in Windows Defender ATP allow security operations personnel to monitor and investigate malicious .settingcontent-ms threats in the network. Using the alert from the antivirus detection, SecOps can pivot to machine timeline and trace the process tree to investigate related events (e.g., file creation) and remediate attacks. Figure 4. Process tree for a sample .settingcontent-ms file extension in machine timeline Advanced Hunting To hunt for possible threats that leverage malicious .settingcontent-ms files in the network, SecOps can use Advanced hunting capabilities in Windows Defender ATP. The Advanced hunting query experience is integrated into the existing Windows Defender ATP investigation experience, making proactive hunting for possible threats more effective. Figure 5. Sample Advanced hunting query that returns file creation events where file name ends with .settingcontent-ms and initiating process is a browser, Outlook, or explorer.exe. As a reminder you can find the above query and many more samples Advanced hunting queries in our GitHub repository.Inside SecOps: Are we impacted by this new APT campaign?
Inside SecOps is a new series of blog posts that we’ll be publishing to showcase how security analysts inside Security Operations Centers (SOC) are leveraging Windows Defender ATP. My name is Milad Aslaner (@MiladMSFT on Twitter) and I’m a security professional in the Windows Defender Advanced Threat Protection (ATP) product engineering team. In my role, I’ve had the privilege of learning how strategic customers are using our product. This has proven to be an invaluable source of feedback which has helped us not only improve existing capabilities but also build new features from the ground up. In this blog post, we’ll focus on two capabilities that can help analysts determine if a newly disclosed Advanced Persistent Threat (APT) campaign affects their organization: Advanced hunting Custom detections Consider the following scenario: You start your day as a security analyst by catching up on e-mails, threat intelligence feeds, and cybersecurity related news. You notice that a newly disclosed indicator of compromise (IOC) has just been disclosed by a computer emergency response team (CERT). According to the CERT report the threat actor group specializes in the same industry as the company you work for. What do you do? The CERT provides a list of known IOC’s. These IOC’s contain malicious file hashes, domains and IP addresses. In order to assess if your organization is affected by the threat, it’s prudent to verify if those IOCs can be found in your environment. Windows Defender ATP is connected to the Microsoft Intelligent Security Graph (ISG). It’s highly likely that the IOCs you’ve just read about are already known and monitored by Microsoft but as any diligent security analyst, you’ll want to verify that. With Advanced hunting and the new custom detection capabilities you can do just that! Advanced hunting allows you to create queries using Kusto Query Language. You’ll have access to 30 days’ worth of raw data where you can run your queries on. When you’ve made your queries, you can save it as a detection logic, which will then raise custom alerts. Example IOCs The following list of IOCs are a subset of a recently disclosed campaign by Unit42, but we’ll use them later to demonstrate how to assess if a threat actor group has made an impact in an organization: Domains:sydwl.cn, dwn.rundll32.ml, w3w.aybc.so, a.ssvs.space IP addresses: 118.24.150.172, 120.55.54.172 Advanced hunting If you are not familiar with Advanced hunting, I recommended that you check out the official documentationand the blog post on how to get started. Based on the sample IOC list in the previous section, I’ve created the following sample query to determine if: any endpoint in the organization had connections to the malicious URLs, any endpoint in the organization had connections to the malicious IP addresses NetworkCommunicationEvents | where RemoteIP in ( "118.24.150.172","120.55.54.172") or RemoteUrl in ("sydwl.cn","dwn.rundll32.ml","w3w.aybc.so","a.ssvs.space") Custom detection Custom detections build on top of Advanced hunting. Essentially instead of running manual queries you can save them as custom detections. Once saved, queries will run automatically every 24 hours, creating alerts complete with your own description, categorization and severity. See the image below for a custom detection rule that raises a high severity alert when it detects any machine in the organization connecting to any of the IP or URLs described in the above query. True Positive After learning about the new APT campaign, you have created an Advanced hunting query and a custom detection. Following these actions, you receive a new alert from your custom detection, indicating a true positive in your environment - alert for an endpoint that has executed one of the known bad files. Alert raised based on newly created custom detection Clicking on the circle next to the domain provides further insights about the domain Clicking on the URL provides more context about the impact within the organization From the alert you are able to go to the respective machine page to further deep dive into the machine timeline. Conclusion As a security analyst in a SecOps team, there are instances when you need to quickly determine if your organization has been impacted by a specific campaign. Advanced hunting is one of the most loved capabilities in Windows Defender ATP which can help meet that need. As demonstrated in the example, all you need to do is create a query to check if there’s been direct impact in your environment. Custom detections build on top of Advanced hunting and lets you quickly transform the queries into custom detections that surface up as alerts in Windows Defender Security Center. Now all that is needed is to get engaged with your network team to block the access to the malicious URLs. I hope you found this tutorial useful. I encourage you to take advantage of the Advanced hunting and custom detection capabilities, provide feedback, or ask questions about this post. Recommended Reading Query data using Advanced hunting in Windows Defender ATP Custom detections overview Getting Started with Windows Defender ATP Advanced Hunting
