June 20, 2021


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A “DFUR-ent” Perspective on Threat Modeling and Application
Log Forensic Analysis

A “DFUR-ent” Perspective on Threat Modeling and Application Log Forensic Analysis

Many organizations operating in e-commerce, hospitality, healthcare,
managed services, and other service industries rely on web
applications. And buried within the application logs may be the
potential discovery of fraudulent use and/or compromise! But, let’s
face it, finding evil in application logs can be difficult and
overwhelming for a few reasons, including:

  • The wide variety of web applications with unique
  • The lack of a standard logging format
  • Logging formats that were designed for troubleshooting
    application issues and not security investigations
  • The need
    for a centralized log analysis solution or SIEM to process and
    investigate a large amount of application log data

So, in this blog post, we discuss threat modeling concepts that can
help prioritize logging decisions and unleash the ability to identify
and investigate attacks against an application. To help us
demonstrate, we’ll describe situations for a fictitious organization
called Dog and Feline Urgent Response, or DFUR, that we presented at the
2020 SANS Digital Forensics & Incident Response (DFIR) Summit

We selected Splunk Enterprise Security (ES) as DFUR’s SIEM and
logging analysis platform, but this is just one option and there are
multiple technologies that can facilitate application log analysis. We
created a Splunk application called “Dog and Feline
Urgent Response (DFUR)
” available on the FireEye GitHub that
contains pre-indexed data and dashboards that you can use to follow
along with the following attack scenarios.

But, enough kitten around. Let’s introduce you to DFUR!

DFUR: Dog and Feline Urgent Response

DFUR is a long-standing organization in the pet wellness industry
that provides care providers, pet owners, and insurance providers with
application services.

  • Care providers, such as veterinarians, use DFUR to process
    patient records, submit prescriptions, and order additional care
  • Pet owners use DFUR to make appointments, pay bills,
    and see diagnostic test results
  • Insurance providers use
    DFUR to receive and pay claims to pet care providers

Application users log into a web portal that forwards logon and user
transaction logs to DFUR’s Splunk ES instance. Backend databases store
metadata for users, such as street addresses and contact information.

DFUR Security Team Threat Modeling

After stumbling through several incidents, the DFUR security team
realized that their application did not log the information needed to
answer investigative question clearly and quickly. The team held
workshops with technical stakeholders to develop a threat model and
improve their application security strategy. They addressed questions,
such as:

  • What types of threats does DFUR face based on industry
  • What impact could those threats have?
  • How
    could the DFUR application be attacked or abused?
  • What log
    data would DFUR need to prove an attack or fraud happened?

The DFUR team compiled the stakeholder feedback and developed a
threat profile to identify and prioritize high-risk threats facing the
DFUR application platform, including:

  • Account takeover and abuse
    • Password attacks (e.g.,
      credential stuffing)
    • Bank account modifications
    • PHI/PII access
    • Health service modifications or
  • Fraudulent reimbursement claim
  • Veterinarians over-prescribing catnip

The DFUR security team discussed how they could identify threats
using their currently available logs, and, well, the findings were not purr-ty.

Logging Problems Identified

The DFUR team used their threat model to determine what log sources
were relevant to their security mission, and then they dug into each
one to confirm the log events were valid, normalized, and accessible.
This effort produced a list of high-priority logging issues that
needed to be addressed before the security team could move forward
with developing methods for detection and analysis:

  • Local logs were not forwarded to their Splunk ES instance.
    Only a limited subset of logging was forwarded to their Splunk ES
    instance, so DFUR analysts couldn’t search for the actions performed
    by users who were authenticated to the application portal.
  • Inaccurate field mapping. DFUR analysts identified extracted
    field values that were mapped to incorrect field names. One example
    was the user-agent in authentication log events had been extracted
    as the username field.
  • Application updates sometimes affected Splunk ingestion and
    DFUR analysts identified servers that didn’t have a
    startup script to ensure log forwarding was enabled upon system
    reboot. Application updates changed the logging output format which
    broke field extractions. DFUR analysts didn’t have a way to
    determine when log sources weren’t operating as expected.
  • Time zone misconfigurations. DFUR analysts determined their
    log sources had multiple time zone configurations which made
    correlation difficult.
  • The log archival settings needed to be modified. DFUR
    analysts needed to configure their Splunk ES instance data
    retirement policy to maintain indexed data for a longer time period
    and archive historical data for quick restoration.
  • Source IP addresses of users logging into the portal were masked
    by a load balancer.
    The DFUR analysts realized that the source
    IP address for every user logon was a load balancer, which made
    attribution even more difficult. The X-Forwarded-For (XFF) field in
    their appliances needed to be enabled.

Analysis Problems Identified

The DFUR infosec team reviewed how previous incidents involving the
DFUR application were handled. They quickly learned that they needed
to solve the following operational issues before they could
effectively investigate application attacks:

  • Inconsistency during manual analysis. DFUR analysts took
    different approaches to searching their Splunk ES instance, and they
    would reach different conclusions. Playbooks were needed to define a
    standard investigative methodology for common incident
  • No documentation of log fields or sources. Some DFUR analysts
    were not aware of all relevant data sources that were available when
    investigating security incidents. This led to findings that were
    based on a small part of the picture. A data dictionary was needed
    that defines the log sources and fields in the DFUR Splunk ES
    instance and the retention time for each log source.
  • Application logs were designed for troubleshooting, not
    . The DFUR application was configured to log
    diagnostic information, application errors, and limited subsets of
    successful user activity. The DFUR team needed to reconfigure and
    develop the application to record more security related events.

DFUR: New and Improved Monitoring and Detection

The DFUR team addressed their application log and analysis problems
and started building a detection and investigative capability in their
Splunk ES instance. Using the analysis workflows developed during the
threat modeling process, the DFUR team designed Splunk dashboards
(Figure 1) to provide detection analytics and context around three
primary datapoints: usernames, IP addresses, and care providers (“organizations”).

A “DFUR-ent” Perspective on Threat Modeling and Application
Log Forensic Analysis

Figure 1: DFUR monitoring and detection dashboard

The DFUR team created the Splunk dashboards using Simple XML to
quickly identify alerts and pivot among the primary datapoints, as
seen in Figure 2. The DFUR team knew that their improved and
streamlined methodology would save time compared to exporting,
analyzing, and correlating raw logs manually.

Figure 2: Pivoting concepts used to
develop DFUR dashboards

Newly armed (legged?) with a monitoring and detection capability,
the DFUR team was ready to find evil!

Attack Scenario #1: Account Takeover

The next morning, the DFUR security team was notified by their
customer service team of a veterinarian provider with the username
‘labradorable’ who hadn’t received their daily claims payment and
noticed their banking information in the DFUR portal was changed overnight.

A DFUR analyst opened the User Activity Enrichment dashboard (Figure
3) and searched for the username to see recent actions performed by
the account.

Figure 3: User Activity Enrichment dashboard

The analyst reviewed the Remote Access Analytics in the dashboard
and identified the following anomalies (Figure 4):

  • The username reminder and password reset action was performed
    the day before from an Indonesia-based IP address
  • The user
    account was logged in from the same suspicious IP address shortly
  • The legitimate user always logs in from California, so
    the Indonesia source IP login activity was highly suspicious

Figure 4: Remote access analytics based on user activity

The DFUR analyst clicked on the Application Activity tab in the User
Activity Enrichment dashboard to see what actions were performed by
the user while they were logged in from the suspicious IP address. The
analyst identified the user account logged in from the suspicious IP
address and performed an email address change and added two (2) new
bank accounts, as seen in Figure 5.

Figure 5: Application activity timeline filtered based on IP address

The DFUR analyst confirmed that the two (2) bank accounts were added
by the user to the care provider with organization ID 754354, as seen
in Figure 6.

Figure 6: Bank accounts added and
assigned to a provider

By clicking on the organization ID in the Splunk results table, the
DFUR analyst triggered a drill-down action to automatically open the
Organization Enrichment Dashboard and populate the organization ID
value with the results from the previous panel (Figure 7). The DFUR
analyst determined that the bank routing information for the new bank
accounts was inconsistent with the organization’s mailing address.  

Figure 7: Organization Enrichment Dashboard

The activity indicated that the attacker had access to the user’s
primary email and successfully reset the DFUR account password. The
DFUR analyst confirmed that no other accounts were targeted by the
suspicious IP address (Figure 8).

Figure 8: IP Address Enrichment dashboard

Attack Scenario #2: Credential Stuffing

Later that afternoon, the DFUR team began receiving reports of
account lockouts in the patient and provider portals when users tried
to login. The security team was asked to investigate potential
password attack activity on their DFUR platform.

The DFUR analyst pulled up the main monitoring and detection
dashboard and scrolled down to the panel focused on identifying
potential password attack activity (Figure 9). They identified five
(5) IP addresses associated with an elevated number of failed login
attempts, suggesting a password spray or credential stuffing attack
with varying success.

Figure 9: Dashboard panel showing potential password attack events

The DFUR analyst clicked on one of the IP addresses which triggered
a drill-down action to open the IP Address Enrichment dashboard and
prepopulate the IP address token value (Figure 10).

Figure 10: IP Address Enrichment dashboard

The DFUR analyst identified more than 3,000 failed login attempts
associated with the IP address with three (3) successful logins that
morning. The Remote Access Analytics panels for the IP address further
showed successful logins for accounts that may have been successfully
compromised and need to be reset (Figure 11).

Figure 11: Remote access analytics for IP address


After implementing the newly developed logs and analysis
capabilities and by leveraging Splunk’s security solutions, the DFUR
security team drastically improved key metrics aligned with their
application security missions:

  1. Identify compromise and fraud before customers report it
  2. Analyze 90% of application security events within 30
  3. Answer all investigation questions from users,
    compliance, and legal teams

Mandiant and the whole DFUR security team hope you can use the
scenarios and references in this post to improve your log analysis and
how you leverage a SIEM solution in the following ways:

  • Reflect on your current logging gaps and capabilities to
  • Enhance logs from “whatever the developers
    implemented” to “designed to be investigated”
  • Develop
    investigative workflows that are reliable and repeatable
  • Correlate pivot points between your data sources and streamline
    correlation capabilities
  • Create monitoring and alerting
    capabilities based on threat modeling
  • Lower the technical
    barrier for comprehensive analysis
  • Implement similar
    analysis capabilities to those in the “DFUR” Splunk application,
    linked in the References section
  • Understand that logs can
    lead into better security analytics and strengthening of your
    security operations


For organizations that utilize Splunk security solutions as their
SIEM solution, for automation, analytics or log aggregation, or want
to try out for free with Splunk’s free trial download, we developed an
application called “Dog and Feline Urgent Response (DFUR)” to
demonstrate application log forensic analysis and dashboard pivoting
concepts. The code contains pre-indexed data and CSV files referenced
by searches contained in four Splunk XML dashboards. All data, such as
IP addresses and usernames, was fabricated for the purposes of the
demo and any association with organizations, users, or pets is coincidental.