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Threat Hunting with Splunk
The document outlines a threat hunting session utilizing Splunk, focusing on detecting and investigating cyber threats through various data sources, including endpoint data and threat intelligence. It details a specific attack scenario involving a malware infection via a weaponized PDF, examining the steps taken to trace the attack back to its root cause. Key methodologies include analyzing network communications, endpoint processes, and using machine learning concepts to improve security posture.
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Threat Hunting with Splunk
- 1. 1 > René Agüeroraguero@splunk.com • > 1 year at Splunk – Security Specialist • Based in Manhattan • 18 years in security – MCSE NT4.0 • CISSP, MSBA – Information Assurance (forensics, auditing and security) • Offensive Security • Exploitation – Metasploit, Web attacks • Rapid7 SE Director $whoami
- 2. Agenda • Threat HutingBasics • Threat Hunting Data Sources • Sysmon Endpoint Data • Cyber Kill Chain • Walkthrough of Attack Scenario Using Core Splunk (hands on) • Enterprise Security Walkthrough • Applying Machine Learning and Data Science to Security
- 3. Log In Credentials January,February & March https://od-threathunting-01.splunkoxygen.com April, May & June https://od-threathunting-02.splunkoxygen.com July and August https://od-threathunting-03.splunkoxygen.com September and October https://od-threathunting-04.splunkoxygen.com November and December https://od-threathunting-05.splunkoxygen.com User: hunter Pass: pr3dator Birth Month
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- 5. Am I inthe right place? Some familiarity with… ● CSIRT/SOC Operations ● General understanding of Threat Intelligence ● General understanding of DNS, Proxy, and Endpoint types of data 5
- 6. This is ahands-on session. The overview slides are important for building your “hunt” methodology 10 minutes - Seriously.
- 7. Threat Hunting withSplunk 7 Vs.
- 8. SANS Threat HuntingMaturity 8 Ad Hoc Search Statistical Analysis Visualization Techniques Aggregation Machine Learning/ Data Science 85% 55% 50% 48% 32% Source: SANS IR & Threat Hunting Summit 2016
- 9. Hunting Tools: InternalData 9 • IP Addresses: threat intelligence, blacklist, whitelist, reputation monitoring Tools: Firewalls, proxies, Splunk Stream, Bro, IDS • Network Artifacts and Patterns: network flow, packet capture, active network connections, historic network connections, ports and services Tools: Splunk Stream, Bro IDS, FPC, Netflow • DNS: activity, queries and responses, zone transfer activity Tools: Splunk Stream, Bro IDS, OpenDNS • Endpoint – Host Artifacts and Patterns: users, processes, services, drivers, files, registry, hardware, memory, disk activity, file monitoring: hash values, integrity checking and alerts, creation or deletion Tools: Windows/Linux, Carbon Black, Tanium, Tripwire, Active Directory • Vulnerability Management Data Tools: Tripwire IP360, Qualys, Nessus • User Behavior Analytics: TTPs, user monitoring, time of day location, HR watchlist Splunk UBA, (All of the above)
- 10. Endpoint: Microsoft SysmonPrimer 10 ● TA Available on the App Store ● Great Blog Post to get you started ● Increases the fidelity of Microsoft Logging Blog Post: http://blogs.splunk.com/2014/11/24/monitoring-network-traffic-with-sysmon-and-splunk/
- 11. User: hunter Pass: pr3dator January,February & March https://od-threathunting-01.splunkoxygen.com April, May & June https://od-threathunting-02.splunkoxygen.com July and August https://od-threathunting-03.splunkoxygen.com September and October https://od-threathunting-04.splunkoxygen.com November and December https://od-threathunting-05.splunkoxygen.com
- 12. Sysmon Event Tags 12 MapsNetwork Comm to process_id Process_id creation and mapping to parentprocess_id
- 13. sourcetype=X* | searchtag=communicate 13
- 14. sourcetype=X* | deduptag| search tag=process 14
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- 16. Demo Story -Kill Chain Framework Successful brute force – download sensitive pdf document Weaponize the pdf file with Zeus Malware Convincing email sent with weaponized pdf Vulnerable pdf reader exploited by malware. Dropper created on machine Dropper retrieves and installs the malware Persistence via regular outbound comm Data Exfiltration Source: Lockheed Martin
- 17. Servers Storage DesktopsEmail Web Transaction Records Network Flows DHCP/ DNS Hypervisor Custom Apps Physical Access Badges Threat Intelligence Mobile CMDB Intrusion Detection Firewall DataLoss Prevention Anti- Malware Vulnerability Scans Traditional Authentication Stream Investigations– chooseyour data wisely 17
- 18. 18 Let’s dig in! Please,raise that hand if you need us to hit the pause button
- 19. APT Transaction FlowAcross Data Sources 19 http (proxy) session to command & control server Remote control Steal data Persist in company Rent as botnet Proxy Conduct Business Create additional environment Gain Access to systemTransaction Threat Intelligence Endpoint Network Email, Proxy, DNS, and Web Data Sources .pdf .pdf executes & unpacks malware overwriting and running “allowed” programs Svchost.exe (malware) Calc.exe (dropper) Attacker hacks website Steals .pdf files Web Portal.pdf Attacker creates malware, embed in .pdf, emails to the target MAIL Read email, open attachment Our Investigation begins by detecting high risk communications through the proxy, at the endpoint, and even a DNS call.
- 20. To begin our investigation,we will start with a quick search to familiarize ourselves with the data sources. In this demo environment, we have a variety of security relevant data including… Web DNS Proxy Firewall Endpoint Email
- 21. Take a lookat the endpoint data source. We are using the Microsoft Sysmon TA. We have endpoint visibility into all network communication and can map each connection back to a process. } We also have detailed info on each process and can map it back to the user and parent process.} Lets get our day started by looking using threat intel to prioritize our efforts and focus on communication with known high risk entities.
- 22. We have multiplesource IPs communicating to high risk entities identified by these 2 threat sources. We are seeing high risk communication from multiple data sources. We see multiple threat intel related events across multiple source types associated with the IP Address of Chris Gilbert. Let’s take closer look at the IP Address. We can now see the owner of the system (Chris Gilbert) and that it isn’t a PII or PCI related asset, so there are no immediate business implications that would require informing agencies or external customers within a certain timeframe. This dashboard is based on event data that contains a threat intel based indicator match( IP Address, domain, etc.). The data is further enriched with CMDB based Asset/identity information.
- 23. We are nowlooking at only threat intel related activity for the IP Address associated with Chris Gilbert and see activity spanning endpoint, proxy, and DNS data sources. These trend lines tell a very interesting visual story. It appears that the asset makes a DNS query involving a threat intel related domain or IP Address. ScrollDown Scroll down the dashboard to examine these threat intel events associated with the IP Address. We then see threat intel related endpoint and proxy events occurring periodically and likely communicating with a known Zeus botnet based on the threat intel source (zeus_c2s).
- 24. It’s worth mentioningthat at this point you could create a ticket to have someone re-image the machine to prevent further damage as we continue our investigation within Splunk. Within the same dashboard, we have access to very high fidelity endpoint data that allows an analyst to continue the investigation in a very efficient manner. It is important to note that near real-time access to this type of endpoint data is not not common within the traditional SOC. The initial goal of the investigation is to determine whether this communication is malicious or a potential false positive. Expand the endpoint event to continue the investigation. Proxy related threat intel matches are important for helping us to prioritize our efforts toward initiating an investigation. Further investigation into the endpoint is often very time consuming and often involves multiple internal hand-offs to other teams or needing to access additional systems. This encrypted proxy traffic is concerning because of the large amount of data (~1.5MB) being transferred which is common when data is being exfiltrated.
- 25. Exfiltration of datais a serious concern and outbound communication to external entity that has a known threat intel indicator, especially when it is encrypted as in this case. Lets continue the investigation. Another clue. We also see that svchost.exe should be located in a Windows system directory but this is being run in the user space. Not good. We immediately see the outbound communication with 115.29.46.99 via https is associated with the svchost.exe process on the windows endpoint. The process id is 4768. There is a great deal more information from the endpoint as you scroll down such as the user ID that started the process and the associated CMDB enrichment information.
- 26. We have aworkflow action that will link us to a Process Explorer dashboard and populate it with the process id extracted from the event (4768).
- 27. This is astandard Windows app, but not in its usual directory, telling us that the malware has again spoofed a common file name. We also can see that the parent process that created this suspicuous svchost.exe process is called calc.exe. This has brought us to the Process Explorer dashboard which lets us view Windows Sysmon endpoint data. Suspected Malware Lets continue the investigation by examining the parent process as this is almost certainly a genuine threat and we are now working toward a root cause. This is very consistent with Zeus behavior. The initial exploitation generally creates a downloader or dropper that will then download the Zeus malware. It seems like calc.exe may be that downloader/dropper. Suspected Downloader/Dropper This process calls itself “svchost.exe,” a common Windows process, but the path is not the normal path for svchost.exe. …which is a common trait of malware attempting to evade detection. We also see it making a DNS query (port 53) then communicating via port 443.
- 28. The Parent Processof our suspected downloader/dropper is the legitimate PDF Reader program. This will likely turn out to be the vulnerable app that was exploited in this attack. Suspected Downloader/Dropper Suspected Vulnerable AppWe have very quickly moved from threat intel related network and endpoint activity to the likely exploitation of a vulnerable app. Click on the parent process to keep investigating.
- 29. We can seethat the PDF Reader process has no identified parent and is the root of the infection. ScrollDown Scroll down the dashboard to examine activity related to the PDF reader process.
- 30. Chris opened 2nd_qtr_2014_report.pdf whichwas an attachment to an email! We have our root cause! Chris opened a weaponized .pdf file which contained the Zeus malware. It appears to have been delivered via email and we have access to our email logs as one of our important data sources. Lets copy the filename 2nd_qtr_2014_report.pdf and search a bit further to determine the scope of this compromise.
- 31. Lets dig alittle further into 2nd_qtr_2014_report.pdf to determine the scope of this compromise.
- 32. Lets search thoughmultiple data sources to quickly get a sense for who else may have have been exposed to this file. We will come back to the web activity that contains reference to the pdf file but lets first look at the email event to determine the scope of this apparent phishing attack.
- 33. We have accessto the email body and can see why this was such a convincing attack. The sender apparently had access to sensitive insider knowledge and hinted at quarterly results. There is our attachment. Hold On! That’s not our Domain Name! The spelling is close but it’s missing a “t”. The attacker likely registered a domain name that is very close to the company domain hoping Chris would not notice. This looks to be a very targeted spear phishing attack as it was sent to only one employee (Chris).
- 34. Root Cause Recap 34 DataSources .pdf executes & unpacks malware overwriting and running “allowed” programs http (proxy) session to command & control server Remote control Steal data Persist in company Rent as botnet Proxy Conduct Business Create additional environment Gain Access to systemTransaction Threat Intelligence Endpoint Network Email, Proxy, DNS, and Web .pdf Svchost.exe (malware) Calc.exe (dropper) Attacker hacks website Steals .pdf files Web Portal.pdf Attacker creates malware, embed in .pdf, emails to the target MAIL Read email, open attachment We utilized threat intel to detect communication with known high risk indicators and kick off our investigation then worked backward through the kill chain toward a root cause. Key to this investigative process is the ability to associate network communications with endpoint process data. This high value and very relevant ability to work a malware related investigation through to root cause translates into a very streamlined investigative process compared to the legacy SIEM based approach.
- 35. 35 Lets revisit thesearch for additional information on the 2nd_qtr_2014- _report.pdf file. We understand that the file was delivered via email and opened at the endpoint. Why do we see a reference to the file in the access_combined (web server) logs? Select the access_combined sourcetype to investigate further.
- 36. 36 The results show54.211.114.134 has accessed this file from the web portal of buttergames.com. There is also a known threat intel association with the source IP Address downloading (HTTP GET) the file.
- 37. 37 Select the IPAddress, left-click, then select “New search”. We would like to understand what else this IP Address has accessed in the environment.
- 38. 38 That’s an abnormallylarge number of requests sourced from a single IP Address in a ~90 minute window. This looks like a scripted action given the constant high rate of requests over the below window. ScrollDown Scroll down the dashboard to examine other interesting fields to further investigate. Notice the Googlebot useragent string which is another attempt to avoid raising attention..
- 39. 39 The requests from52.211.114.134 are dominated by requests to the login page (wp-login.php). It’s clearly not possible to attempt a login this many times in a short period of time – this is clearly a scripted brute force attack. After successfully gaining access to our website, the attacker downloaded the pdf file, weaponized it with the zeus malware, then delivered it to Chris Gilbert as a phishing email. The attacker is also accessing admin pages which may be an attempt to establish persistence via a backdoor into the web site.
- 40. Kill Chain AnalysisAcross Data Sources 40 http (proxy) session to command & control server Remote control Steal data Persist in company Rent as botnet Proxy Conduct Business Create additional environment Gain Access to systemTransaction Threat Intelligence Endpoint Network Email, Proxy, DNS, and Web Data Sources .pdf .pdf executes & unpacks malware overwriting and running “allowed” programs Svchost.exe (malware) Calc.exe (dropper) Attacker hacks website Steals .pdf files Web Portal.pdf Attacker creates malware, embed in .pdf, emails to the target MAIL Read email, open attachment We continued the investigation by pivoting into the endpoint data source and used a workflow action to determine which process on the endpoint was responsible for the outbound communication. We Began by reviewing threat intel related events for a particular IP address and observed DNS, Proxy, and Endpoint events for a user in Sales. Investigation complete! Lets get this turned over to Incident Reponse team. We traced the svchost.exe Zeus malware back to it’s parent process ID which was the calc.exe downloader/dropper. Once our root cause analysis was complete, we shifted out focus into the web logs to determine that the sensitive pdf file was obtained via a brute force attack against the company website. We were able to see which file was opened by the vulnerable app and determined that the malicious file was delivered to the user via email. A quick search into the mail logs revealed the details behind the phishing attack and revealed that the scope of the compromise was limited to just the one user. We traced calc.exe back to the vulnerable application PDF Reader.
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- 42. Other Items ToNote Items to Note Navigation - How to Get Here Description of what to click on Click
- 43. Key Security Indicators(build your own!) Sparklines Editable
- 44. Various ways tofilter data Malware-Specific KSIs and Reports Security Domains -> Endpoint -> Malware Center
- 45. Filterable KSIs specific toRisk Risk assigned to system, user or other Under Advanced Threat, select Risk Analysis
- 46. (Scroll Down) Recent RiskActivity Under Advanced Threat, select Risk Analysis
- 47. Filterable, down toIoC KSIs specific to Threat Most active threat source Scroll down… Scroll Under Advanced Threat, select Threat Activity
- 48. Specifics about recentthreat matches Under Advanced Threat, select Threat Activity
- 49. To add threatintel go to: Configure -> Data Enrichment -> Threat Intelligence Downloads Click
- 50. Click “Threat Artifacts” Under“Advanced Threat” Click
- 51. Artifact Categories – clickdifferent tabs… STIX feed Custom feed Under Advanced Threat, select Threat Artifacts
- 52. Review the AdvancedThreat content Click
- 53. Data from assetframework Configurable Swimlanes Darker=more events All happened around same timeChange to “Today” if needed Asset Investigator, enter “192.168.56.102”
- 54. Data Science & MachineLearning In Security 54
- 55. Disclaimer: I amnot a data scientist
- 56. Types of MachineLearning Supervised Learning: generalizing from labeled data
- 57. Supervised Machine Learning 57 DomainName TotalCnt RiskFactor AGD SessionTime RefEntropy NullUa Outcome yyfaimjmocdu.com 144 6.05 1 1 0 0 Malicious jjeyd2u37an30.com 6192 5.05 0 1 0 0 Malicious cdn4s.steelhousemedia.com 107 3 0 0 0 0 Benign log.tagcade.com 111 2 0 1 0 0 Benign go.vidprocess.com 170 2 0 0 0 0 Benign statse.webtrendslive.com 310 2 0 1 0 0 Benign cdn4s.steelhousemedia.com 107 1 0 0 0 0 Benign log.tagcade.com 111 1 0 1 0 0 Benign
- 58. Unsupervised Learning: generalizingfrom unlabeled data
- 59. Unsupervised Machine Learning •No tuning • Programmatically finds trends • UBA is primarily unsupervised • Rigorously tested for fit 59 AlgorithmRaw Security Data Automated Clustering
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- 61. ML Toolkit &Showcase • Splunk Supported framework for building ML Apps – Get it for free: http://tiny.cc/splunkmlapp • Leverages Python for Scientific Computing (PSC) add-on: – Open-source Python data science ecosystem – NumPy, SciPy, scitkit-learn, pandas, statsmodels • Showcase use cases: Predict Hard Drive Failure, Server Power Consumption, Application Usage, Customer Churn & more • Standard algorithms out of the box: – Supervised: Logistic Regression, SVM, Linear Regression, Random Forest, etc. – Unsupervised: KMeans, DBSCAN, Spectral Clustering, PCA, KernelPCA, etc. • Implement one of 300+ algorithms by editing Python scripts
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- 65. Splunk UBA UseCases ACCOUNT TAKEOVER • Privileged account compromise • Data exfiltration LATERAL MOVEMENT • Pass-the-hash kill chain • Privilege escalation SUSPICIOUS ACTIVITY • Misuse of credentials • Geo-location anomalies MALWARE ATTACKS • Hidden malware activity BOTNET, COMMAND & CONTROL • Malware beaconing • Data leakage USER & ENTITY BEHAVIOR ANALYTICS • Suspicious behavior by accounts or devices EXTERNAL THREATSINSIDER THREATS
- 66. Splunk User BehaviorAnalytics (UBA) • ~100% of breaches involve valid credentials (Mandiant Report) • Need to understand normal & anomalous behaviors for ALL users • UBA detects Advanced Cyberattacks and Malicious Insider Threats • Lots of ML under the hood: – Behavior Baselining & Modeling – Anomaly Detection (30+ models) – Advanced Threat Detection • E.g., Data Exfil Threat: – “Saw this strange login & data transferfor user kwestin at 3am in China…” – Surface threat to SOC Analysts
- 67. Workflow Raw Events 1 Statistical methods Securitysemantics 2 Threat Models Lateral movement ML Patterns Sequences Beaconing Land-speed violation Threats Kill chain sequence 5 Supporting evidence Threat scoring Graph Mining 4 Continuousself-learning Anomalies graph Entity relationship graph 3 Anomalies
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- 69. 1:1 Security Workshops ●Threat Intelligence Workshop ● CSC 20 Workshop ● SIEM+ ● Splunk UBA Data Science Workshop ● Enterprise Security Benchmark Assessment
- 70.
Editor's Notes
- #5 Now unfortunately, you do need a modern laptop with a modern browser to participate. You can probably get away with a Surface or something like that, but iPads, old browsers, and especially IBM PCjr’s will not work. (don’t laugh – I actually had one of those.)
- #7 We are going to get hands on, and I want to make sure we have enough time to go through the exercises. But I need to frame up why this is important first, so bear with me. -Why did we decide to do a hands on session -compelling presentations, especially from Splunk customers but…. -we want to get our hands on the software and experience it for ourselves
- #11 Great Blog Post covering the Microsoft Sysmon TA (download, config, install, usage): http://blogs.splunk.com/2014/11/24/monitoring-network-traffic-with-sysmon-and-splunk/ The App TS is located here: https://apps.splunk.com/apps/#/search/sysmon/page/1
- #14 Given a general timeframe and a network 5-tuple (src_ip dest_ip, src_port. Dest_port, and protocol) we can query our mountain of endpoint data VERY QUICKLY to figure out who/what was responsible for the communication.
- #16 Armed with an understanding of the general methodologies that the attacker utilizes in modern Advanced Persistent Threats (APT), we can better equip ourselves to defend and disrupt this type of attack. The above phases are described by the Lockheed Martin Cyber Kill Chain. Our goal is to make certain that we choose our data sources wisely such that we have covered across all of the phases to give us the best chance or defending ourselves. We overlay both CMDB and Threat Intelligence based lookups to enrich that data to act as both context to help prioritize our efforts and act as a detection mechanism in this example. Keep in mind, this exercise is focused on investigations (hunting) and not on detection. Threat Intelligence will likely not look as clean as it does in this lab and will usually contain a good deal of false positives depending on the fidelity of the threat intelligence source.
- #17 Here is the scenario that we will be walking through in this lab.
- #18 So to find all of the “known” threats – that’s pretty simple. We’re going to grab all of the data from all of the things in your environment that are regularly updated with knowledge about known threats. All the traditional security stuff. IDS/IPS. Anti-malware defenses. DLP, Vulnerability scans. SIEM technology. And of course we will collect firewall data and auth data. But what about unknown threats? How do we find those? Well we need to look at a much bigger set of data, and then find the unusual patterns in that data. So we want to look at things like threat intel, email, web, desktops – the first four items on the top line are what we’ll focus on in this hands-on exercise. But note that Splunk can collect a whole lot more data that we believe is extremely security relevant – especially when it comes to detecting those unknown threats.
- #23 Cymru is pronounced (“cum-ree”)
- #43 This should look familiar to you. What we’re doing here is giving a starting point for any Security Analyst to understand at a high level what’s going on in the environment. A single pane of glass, if you will, for all security data. Everything we are seeing here is customizable – the panels, the indicators, via standard Splunk functionality. Most of the data on this dashboard is centered on Notable Events. Notable Events are a concept unique to Splunk with ES – there’s an entire Notable Event framework that allows us to perform simple or complex correlations, and then create events by analyzing disparate events from disparate sources. Notable Events in ES are categorized into various high-level security domains: access, audit, identity, network, and threat. We’ll see those categories throughout the app. You can see Splunk Sparklines here – these little green lines. These are great for detecting quick trends in the security events – a continuous line means something constant, which could be a heartbeat or a scripted attack. A spike could be a single attack or maybe just someone fat-fingering their password a few times. We’ll drill into some of these incidents in a few minutes, but let’s continue on with our tour. How does all this data get into Splunk?
- #44 This should look familiar to you. What we’re doing here is giving a starting point for any Security Analyst to understand at a high level what’s going on in the environment. A single pane of glass, if you will, for all security data. Everything we are seeing here is customizable – the panels, the indicators, via standard Splunk functionality. Most of the data on this dashboard is centered on Notable Events. Notable Events are a concept unique to Splunk with ES – there’s an entire Notable Event framework that allows us to perform simple or complex correlations, and then create events by analyzing disparate events from disparate sources. Notable Events in ES are categorized into various high-level security domains: access, audit, identity, network, and threat. We’ll see those categories throughout the app. You can see Splunk Sparklines here – these little green lines. These are great for detecting quick trends in the security events – a continuous line means something constant, which could be a heartbeat or a scripted attack. A spike could be a single attack or maybe just someone fat-fingering their password a few times. We’ll drill into some of these incidents in a few minutes, but let’s continue on with our tour. How does all this data get into Splunk?
- #45 Here we have a simple dashboard showing us all sorts of detail about recent malware activity in the environment. Like Security Posture, this is high level information, but more granular about a certain security domain (Malware, which is under Endpoint). We have these “centers” throughout ES for things like Access, Traffic, Intrusions, Updates, Vulnerabilities, and many other security-relevant areas, and you can investigate them later. For now, let’s drill into two of the “top infections” to see CIM at work. Looking at this dashboard we can’t tell that we actually have at least two different endpoint protection systems feeding data into Splunk: Sophos, Trend Micro, and Symantec Endpoint Protection. Splunk normalizes the data on search time, according to CIM, to create this (and the other) dashboards. Click on Mal/Packer, and you’ll see that this infection was detected by Sophos. The raw logs are literally a click away:
- #46 The main reason why this risk framework is important is that it gets you away from writing specific rules for specific threats or assets. You don’t need 1,000 correlation rules anymore – you simply can elevate risk scores on whatever object you want, based on the behavior you’re seeing in the environment. So the idea here is, a correlation rule fires, and then a risk modifier takes effect and changes the risk score based on cumulative scoring of whatever else has happened to that user, or system, or other object. On the dashboard, we can define filters to find a particular system or user or timeframe. Note the natural language descriptions (in the screenshot they are medium and low). We track how your overall risk scoring is doing over time, and constantly re-calculate the baseline. Got a lot of activity going on that isn’t “normal” for that timeframe and you might see things going from “increasing minimally” to “extremely increasing” – all based on what the historical norm is. We can of course see which objects have the highest risk and which correlation rules are contributing the most to the highest risk.
- #48 On the dashboard we can see that we’re using the power of Splunk search to match artifacts in our incoming data against IoC’s we find in our threat feeds. Splunk de-duplicates the threat feeds so that if an artifact shows up in multiple feeds you don’t get duplicate notifications. We can filter the display by threat_group, which is essentially the source of the IoCs. This could be something commercial like ThreatStream or ThreatConnect or Norse, something open-source like Sans or iblocklist, or something from your ISAC that is delivered over a TAXII feed in STIX format. The threat collection shows that we can use various IoCs to match up against artifacts in our data – IP addresses, domain names, URLs, filenames, certificate common names and organizations, email addresses, registry keys – as long as it can be defined in your incoming feed or locally, you can use it as an IoC. You can see the most active threat sources, and if you scroll down, you can see the most recent matches against your threat feeds. How are these configured? Let’s go to the configuration, and see.
- #51 In version 3.1 of Enterprise Security we introduced a full Risk Analysis framework. This is unique because we allow you to assign an arbitrary risk number, that means something to you, based on a notable event. You can assign risk to a user, or to a system, or to some other object that you see in the environment – perhaps a particular piece of malware is considered risky to you so you elevate the risk on the malware “object” itself. Let’s bring up the Risk Analysis page associated with Advanced Threat:
- #52 Rounding out the Threat Intelligence capabilities are the Threat Artifacts browser, which allows us to search through all of the artifacts stored in ES:
- #53 We don’t have time to go through each and every one of the advanced threat capabilities in the ES app. However, let’s just see that up here under Advanced Threat we have some very interesting capabilities: Some of the most useful ones are the Protocol Intelligence that leverages wire data from things like Splunk Stream, Netflow, and Bro. Also the Access Anomalies and User Activity, which are very useful to detect possible insider threat. And the New Domain Analysis, which analyzes traffic patterns and DNS queries to domains, and then tells you if you have devices communicating with recently registered garbage domains (that are often associated with DGA). Again – this is something you can go through on your own time.
- #54 Asset Investigator shows us, at the top, all of the things we know about this asset from sources such as CMDBs or Active Directory. It also has multiple “swimlanes” that visually show you what’s been going on with the asset: We can see Threat List, Exec File, IDS, and Notable Events associated with this asset, most of those happening right around the same time (this was likely the time of infection).
- #56 More beaker than bunson
- #57 Supervised learning is where you have existing LABELS in the data to help you out. Example: If you’re training a model for CUSTOMER CHURN, historically you know which customers stayed and which left. You can build a model to correlate historical churn with other features in the data. Then you can PREDICT churn for each customer based on everything they’re doing in real-time and have done in the past.
- #59 Unsupervised learning is where you have NO LABELS to help you out. You have to figure out patterns Example: If you’re trying to do BEHAVIORAL ANALYTICS, you might just have a big confusing pile of IT & Security data to wade through. Unsupervised learning is the art & science of finding PATTERNS, BASELINES and ANOMALIES in the data. Once you understand all this (that’s hard!) you can try to predict possible INCIDENTS and THREATS. Good ML involves FEEDBACK loops. Best bet is to incorporate INCIDENT RESPONSE data and learn from what analysts have done in the past.
- #62 Free app! Toolkit & PSC are both free. Go to ML App link above, and click Documentation. Links for all distros () Q: Why standalone SH? A: Don’t want ML exploration & production to bring down other Splunk workloads Can use standalone 6.4 SH with older version SH cluster & indexers.
- #67 Re: 100% of breaches involve valid credentials: "Mandiant is the leader in incident response. They are the best of the best. They're brought in to deal with the largest, highest profile, most damaging breaches. When you read a news headline about a large organization being compromised, there is a great chance that Mandiant is working behind the scenes to eradicate the attackers from the environment. In a recent yearly report-when they looked across all the very damaging attacks they responded to-they noticed that valid credentials were used at some point in every single one of them. Why do we care? Well we care because it means that we cannot use simple techniques like counting failed logins to detect an attack. In fact based on this stat, there may not even be any failed logins to count! Instead we need to be much smarter. For example how can we look at 1000 successful logins and determine which of them was the malicious one? We do that through behavior analytics, baseline/outlier, ML, etc."