Threat Hunting with Splunk

Threat Hunting with Splunk
Presenter: Ken Westin, M.Sc, OSCP
Splunk, Security Market Specialist

Agenda
• Threat Huting Basics
• 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

Log In Credentials
January, February & March https://od-how-calgary2.splunkoxygen.com
April, May & June https://od-how-calgary3.splunkoxygen.com
July, August & September https://od-how-calgary4.splunkoxygen.com
October, November & December https://od-how-calgary5.splunkoxygen.com
User: hunter
Pass: pr3dator

Am I in the right place?
Some familiarity with…
● CSIRT/SOC Operations
● General understanding of Threat Intelligence
● General understanding of DNS, Proxy, and Endpoint types of data
5

This is a hands-on session.
The overview slides are important for building your
“hunt” methodology
10 minutes - Seriously.

Threat Hunting with Splunk
8
Vs.

SANS Threat Hunting Maturity
9
Ad Hoc
Search
Statistical
Analysis
Visualization
Techniques
Aggregation Machine Learning/
Data Science
85% 55% 50% 48% 32%
Source: SANS IR & Threat Hunting Summit 2016

Hunting Tools: Internal Data
10
• 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)

Endpoint: Microsoft Sysmon Primer
12
● 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/

Sysmon Event Tags
14
Maps Network Comm to process_id
Process_id creation and mapping to parentprocess_id

sourcetype=X* | search tag=communicate
15

sourcetype=X* | dedup tag| search tag=process
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

Servers
Storage
DesktopsEmail Web
Transaction
Records
Network
Flows
DHCP/ DNS
Hypervisor
Custom
Apps
Physical
Access
Badges
Threat
Intelligence
Mobile
CMDB
Intrusion
Detection
Firewall
Data Loss
Prevention
Anti-
Malware
Vulnerability
Scans
Traditional
Authentication
Stream Investigations – choose your data wisely
19

20
Let’s dig in!
Please, raise that hand if you need us to hit the pause button

APT Transaction Flow Across Data Sources
21
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.

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

Take a look at 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.

We have multiple source
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.

We are now looking 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.
Scroll Down
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).

It’s worth mentioning that 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.

Exfiltration of data is 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.

We have a workflow action that will
link us to a Process Explorer
dashboard and populate it with the
process id extracted from the event
(4768).

This is a standard 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.

The Parent Process of 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.

We can see that the PDF
Reader process has no
identified parent and is the
root of the infection.
Scroll Down
examine activity related to the PDF
reader process.

Chris opened 2nd_qtr_2014_report.pdf
which was 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.

Lets dig a little further into
2nd_qtr_2014_report.pdf to determine the scope
of this compromise.

Lets search though multiple 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.

We have access to 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).

Root Cause Recap
36
Data Sources
to
command & control
server
Remote control
Steal data
Persist in company
Rent as botnet
Proxy
Conduct
Business
Create additional
environment
Gain Access
Threat
Intelligence
Endpoint
Network
Email, Proxy,
DNS, and Web
.pdf
Svchost.exe
(malware)
Calc.exe
(dropper)
Steals .pdf files
Web
Portal.pdf
Attacker creates
emails
to the target
MAIL
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.

37
Lets revisit the search 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.

38
The results show 54.211.114.134 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.

39
Select the IP Address, left-click, then
select “New search”. We would like to
understand what else this IP Address
has accessed in the environment.

40
That’s an abnormally large
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.
Scroll Down
examine other interesting fields to
further investigate.
Notice the Googlebot
useragent string which is
another attempt to avoid
raising attention..

41
The requests from 52.211.114.134 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.

Kill Chain Analysis Across Data Sources
42
to
command & control
server
Remote control
Steal data
Persist in company
Rent as botnet
Proxy
Conduct
Business
Create additional
environment
Gain Access
Threat
Intelligence
Endpoint
Network
Email, Proxy,
DNS, and Web
Data Sources
.pdf
Svchost.exe
(malware)
Calc.exe
(dropper)
Steals .pdf files
Web
Portal.pdf
Attacker creates
emails
to the target
MAIL
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.

Want to Follow Along?
Optional – For Machine Learning Component
● Download Splunk
http://www.splunk.com/en_us/download-21.html
● Install the Machine Learning Toolkit
http://tiny.cc/splunkmlapp
● Install the Python for Scientific Computing app
https://splunkbase.splunk.com/app/2881/ (Mac)
https://splunkbase.splunk.com/app/2883/ (Windows)

46
Ad Hoc
Search
Statistical
Analysis
Visualization
Techniques
Data Science
85% 55% 50% 48% 32%

Splunk is the Security Nerve Center
App
Servers
Network
Threat Intelligence
Firewall
Web Proxy
Internal Network
Security
Identity
Endpoints

Gartner Magic Quadrant for SIEM
48
Incident Response
User Monitoring
Data & App Monitoring
Advanced Analytics
Basic Security Monitoring
Advanced Threat Detection
Forensic & Incident Response
Real-Time Monitoring
Advanced Threat
Defense
Business Context &
Security Intelligence
Deployment &
Support Simplicity

Other Items To Note
Items to Note
Navigation - How to Get Here
Description of what to click on
Click

Key Security Indicators (build your own!)
Sparklines
Editable

Various ways to filter data
Malware-Specific KSIs and Reports
Security Domains -> Endpoint -> Malware Center

Filterable
KSIs specific to Risk
Risk assigned to system,
user or other
Under Advanced Threat,
select Risk Analysis

(Scroll Down)
Recent Risk Activity
select Risk Analysis

Filterable, down to IoC
KSIs specific to Threat
Most active threat source
Scroll down…
Scroll
select Threat Activity

Specifics about recent threat matches
select Threat Activity

To add threat intel go to:
Configure -> Data Enrichment ->
Threat Intelligence Downloads
Click

Click “Threat Artifacts”
Under “Advanced Threat”
Click

Artifact Categories –
click different tabs…
STIX feed
Custom feed
select Threat Artifacts

Review the Advanced Threat
content
Click

Data from asset framework
Configurable Swimlanes
Darker=more events
All happened around same timeChange to
“Today” if needed
Asset Investigator, enter
“192.168.56.102”

Data Science &
Machine Learning In
Security
62

Evolution of Security Correlation to Advanced Analytics
63
Two-Dimensional - Correlation: Use of regex and pattern matching for strings. Used in anti-
malware, IDS/IPS, DLP and basic SIEM. Use of string matching to search a binary file to
identify type of threat. Enhanced capability to identify previously known threats and host
enumeration within an environment.
Multi-Dimensional - Analytics : Hybrid model developed as adversaries learned to
circumvent basic correlation and to reduce false positives. Thresholds and combinations of
rules developed. Starting to create behavioral models, statistical analysis and pattern
identification not just based on signatures.
One-Dimensional - Correlation: Fast and efficient basic matching of domains,
IP addresses, user-agent, MD5 file hashes. Use of Boolean operators to identify if signature
is on a black/white list. Common usage in most firewall and IDS tools.
N-Dimensional Advanced - Analytics: Shift away from heavy manual tagging and rule
building alone, leverages advanced and predictive analytics, machine learning, graph
analysis and elements of data science to enhance the analyst to identify previously
unknown threats, shift from correlation to causation.

64
Ad Hoc
Search
Statistical
Analysis
Visualization
Techniques
Data Science
85% 55% 50% 48% 32%

Disclaimer: I am not a data scientist

Types of Machine Learning
Supervised Learning: generalizing from labeled data

Supervised Machine Learning
67
Domain Name 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

Unsupervised Learning: generalizing from unlabeled data

Unsupervised Machine Learning
• No tuning
• Programmatically finds trends
• UBA is primarily unsupervised
• Rigorously tested for fit
69
AlgorithmRaw Security Data Automated Clustering

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

Machine Learning
Toolkit Demo
72

Splunk UBA Use Cases
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

Splunk User Behavior Analytics (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

Workflow
Raw Events
1
Statistical methods
Security semantics
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

Security Workshops
● Security Readiness Assessments (CSC 20)
● Splunk UBA Data Science Workshop
● Enterprise Security Benchmark Assessment
● Insider Threat

Security Workshop Survey
https://www.surveymonkey.com/r/KFVLF37

Threat Hunting with Splunk

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