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   * Adversaries Can “Log In with Microsoft” through the nOAuth Azure Active
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     Welcome to the Adversary Universe Podcast: Unmasking the Threat Actors
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     Movin’ Out: Identifying Data Exfiltration in MOVEit Transfer Investigations
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     Adversaries Can “Log In with Microsoft” through the nOAuth Azure Active
     Directory Vulnerability
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     Relentless Threat Activity Puts Identities in the Crosshairs
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     CrowdStrike Extends Identity Security Innovations to Protect Customers and
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     Attackers Set Sights on Active Directory: Understanding Your Identity
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     07/08/2020
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     Making Sense of the Dark Web with Falcon Intelligence Recon+
     06/09/2023
     
     Hypervisor Jackpotting, Part 3: Lack of Antivirus Support Opens the Door to
     Adversary Attacks
     05/15/2023
     
     CrowdStrike Falcon Platform Detects and Prevents Active Intrusion Campaign
     Targeting 3CXDesktopApp Customers
     03/29/2023
     
     QakBot eCrime Campaign Leverages Microsoft OneNote Attachments
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     05/15/2023
     
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 * Start Free Trial
   * Endpoint & Cloud Security
     Endpoint & Cloud Security
     
     Welcome to the Adversary Universe Podcast: Unmasking the Threat Actors
     Targeting Your Organization
     07/13/2023
     
     CrowdStrike Expands XDR Ecosystem to Give Customers a Data Advantage
     07/13/2023
     
     July 2023 Patch Tuesday: Six Actively Exploited Zero-Days and Nine Critical
     Vulnerabilities Identified
     07/11/2023
     
     Why Customers Are Consolidating Cybersecurity with CrowdStrike
     07/10/2023
   * Engineering & Tech
     Engineering & Tech
     
     How CrowdStrike Uses Similarity-Based Mapping to Understand Cybersecurity
     Data and Prevent Breaches
     06/28/2023
     
     Cracking the Code of AI Decision Making: Harnessing the Power of SHAP
     Values
     06/13/2023
     
     CrowdStrike’s Artificial Intelligence Tooling Uses Similarity Search to
     Analyze Script-Based Malware Attack Techniques
     03/23/2023
     
     CrowdStrike’s Free TensorFlow-to-Rust Conversion Tool Enables Data
     Scientists to Run Machine Learning Models as Pure Safe Code
     03/02/2023
   * Executive Viewpoint
     Executive Viewpoint
     
     CrowdStrike Named a Leader with “Exceptional” MDR Service: 2023 Forrester
     Wave for MDR
     05/18/2023
     
     CrowdStrike and Dell: Making Cybersecurity Fast and Frictionless
     03/23/2023
     
     Three Times a Leader: CrowdStrike Named a Leader in Gartner® Magic
     Quadrant™ for Endpoint Protection Platforms
     03/02/2023
     
     CrowdStrike 2023 Global Threat Report: Resilient Businesses Fight
     Relentless Adversaries
     02/28/2023
   * From The Front Lines
     From The Front Lines
     
     Business as Usual: Falcon Complete MDR Thwarts Novel VANGUARD PANDA (Volt
     Typhoon) Tradecraft
     06/22/2023
     
     Discovering the MOVEit Vulnerability with the CrowdStrike Falcon Platform
     06/21/2023
     
     Adversaries Go Hands-On in Japan: Know the Threat and Know the Solution
     06/12/2023
     
     Movin’ Out: Identifying Data Exfiltration in MOVEit Transfer Investigations
     06/05/2023
   * Identity Protection
     Identity Protection
     
     Adversaries Can “Log In with Microsoft” through the nOAuth Azure Active
     Directory Vulnerability
     07/14/2023
     
     Relentless Threat Activity Puts Identities in the Crosshairs
     05/01/2023
     
     CrowdStrike Extends Identity Security Innovations to Protect Customers and
     Stop Breaches
     03/20/2023
     
     Attackers Set Sights on Active Directory: Understanding Your Identity
     Exposure
     12/14/2022
   * Observability & Log Management
     Observability & Log Management
     
     How to Augment or Replace Your SIEM with the CrowdStrike Falcon Platform
     07/11/2023
     
     Top 5 SIEM Use Cases CrowdStrike Falcon LogScale Solves Today
     06/23/2023
     
     Introducing CrowdStream: Simplifying XDR Adoption and Solving Security’s
     Data Challenge
     04/21/2023
     
     Make Compliance a Breeze with Modern Log Management
     02/07/2023
   * People & Culture
     People & Culture
     
     Supporting Our Heroes: SkillBridge Program Connects Veterans with
     CrowdStrike Internships
     06/06/2023
     
     VP of Legal Jeanne Miller-Romero on Women’s History Month and Being a Woman
     in Leadership
     03/22/2023
     
     What International Women’s Day Means to Women of CrowdStrike
     03/07/2023
     
     What Martin Luther King Jr. Day Means to Leaders of CrowdStrike’s Black
     Employee Resource Group
     01/13/2023
   * Remote Workplace
     Remote Workplace
     
     CrowdStrike Changes Designation of Principal Executive Office to Austin,
     Texas
     12/28/2021
     
     CrowdStrike and EY Join Forces to Boost Organizational Resiliency
     05/24/2021
     
     Go Beyond the Perimeter: Frictionless Zero Trust With CrowdStrike and
     Zscaler
     03/29/2021
     
     Flexible Policy Management for Remote Systems
     07/08/2020
   * Research & Threat Intel
     Research & Threat Intel
     
     Making Sense of the Dark Web with Falcon Intelligence Recon+
     06/09/2023
     
     Hypervisor Jackpotting, Part 3: Lack of Antivirus Support Opens the Door to
     Adversary Attacks
     05/15/2023
     
     CrowdStrike Falcon Platform Detects and Prevents Active Intrusion Campaign
     Targeting 3CXDesktopApp Customers
     03/29/2023
     
     QakBot eCrime Campaign Leverages Microsoft OneNote Attachments
     03/17/2023
   * Tech Center
     Tech Center
     
     How to Complete Your LogScale Observability Strategy with Grafana
     05/15/2023
     
     Securing private applications with CrowdStrike Zero Trust Assessment and
     AWS Verified Access
     04/18/2023
     
     How to Manage USB Devices
     03/22/2023
     
     How to Speed Investigations with Falcon Forensics
     03/10/2023


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LEVERAGING THE DARK SIDE: HOW CROWDSTRIKE BOOSTS MACHINE LEARNING EFFICACY
AGAINST ADVERSARIES

May 9, 2023

Denis Rozimovschii Endpoint & Cloud Security

 * Adversarial machine learning (ML) attacks can compromise a ML model’s
   effectiveness and ability to detect malware through strategies such as using
   static ML evasion to modify known malware variants
 * CrowdStrike improves detection capabilities by red teaming our own ML malware
   classifiers using automated tools that generate new adversarial samples 
 * CrowdStrike’s Adversarial Pipeline can automatically generate millions of
   unique adversarial samples based on a series of generators with configurable
   attacks
 * Using new, out-of-sample adversarial samples in ML model training data has
   shown a 19% increase in retention of malware samples at high confidence
   levels 

The power of the CrowdStrike Falcon® platform lies in its ability to detect and
protect customers from new and unknown threats by leveraging the power of the
cloud and expertly built machine learning (ML) models.

In real-world conditions and in independent third-party evaluations, Falcon’s
on-sensor and cloud ML capabilities consistently achieve excellent results
across Windows, Linux and macOS platforms. This is especially impressive given
ML uses no signatures, enabling the Falcon platform to identify malicious intent
based solely on file attributes. The results reflect the effectiveness of
CrowdStrike’s multilevel ML approach, which incorporates not only file analysis
but also behavioral analysis and indicators of attack (IOAs). 

However, ML is not infallible. It is susceptible to adversarial attacks from
humans and from other ML algorithms. Examples of the latter include introducing
compromised data during the training process or subtly modifying existing
malware versions.

CrowdStrike’s Adversarial Pipeline is a tool that combats one of the most
commonly used adversarial ML tactics: static ML evasion. Our research team can
use this pipeline to generate a large volume of new and unique adversarial
samples using a series of generators with configurable attacks to simulate new
and modified versions of known malware. These samples are used to train our ML
models to significantly increase their efficacy in detecting cyberattacks that
employ static ML evasion. 

The industry-leading CrowdStrike Falcon platform sets the new standard in
cybersecurity. Watch this demo to see the Falcon platform in action.


THE GROWING THREAT OF ADVERSARIAL ML ATTACKS   

As recently as five years ago, adversarial ML attacks were relatively rare, but
by 2020, the threat had increased to the point that MITRE released its ATLAS
(Adversarial Threat Landscape for Artificial-Intelligence Systems) Threat
Matrix. 

Adversarial ML, or adversarial attacks, involve a broad set of methods to trick
ML into providing unexpected or wrong outputs. Some examples are:

 1. Forcing a classifier to say a picture of a panda is a gibbon by making
    targeted mathematical modifications to the panda image
 2. Appending some text extracted from a clean Windows executable to a detected
    malicious file to fool a classifier into thinking it’s clean
 3. Breaching and sneaking into the internal network of a company and inserting
    a few million pictures of traffic lights that are labeled as “duck” into the
    image database

Attacks can be executed from multiple stages of the model’s development life
cycle. Data can be weaponized during training by injecting incorrectly labeled
data into the training set, as MITRE states, or during classification by
crafting data that would exploit the model (and any others subsequently trained
using that corpus). Existing malware can be modified to evade detection by ML
models scanning for specific signatures. Not surprisingly, these methods can be
chained, combined and merged to increase the effectiveness of the attack. The
result of a successful adversarial ML attack is malware that is able to
stealthily evade a trained and production-ready ML model.

It’s no wonder the subject of adversarial attacks has gained so much interest
across the field of ML, including malware detection, object recognition,
autonomous driving, medical systems and other applications. Researchers across
the ML spectrum have been working to improve model robustness and detect
adversarial attacks based on malicious input.

In security terms, fully undetectable malware is malicious software never before
seen in the wild. It therefore cannot be detected by antivirus software that
relies on a database of known virus definitions or signatures. Modifying samples
of existing malware to achieve fully undetectable malware or to avoid a
designated antivirus detection (static or ML-enabled) is one of the oldest
tricks in the book used by red teams and attackers. 

Small, targeted changes in the analyzed sample can lead to drastically different
results in detection efficacy. Static, rule-based detections are also
susceptible to this type of attack, which has been successfully applied in the
wild by Emotet and other sophisticated threat actors. 

A research paper published in March 2022 outlines the challenge in combating
this form of adversarial malware:

“The traditional approach — based on analysis of static signatures of the
malware binary (e.g., hashes) — is increasingly rendered ineffective by
polymorphism and the widespread availability of program obfuscation tools. Using
such tools, malware creators can quickly generate thousands of binary variants
of functionally identical samples, effectively circumventing signature-based
approaches.”

CrowdStrike has stringent processes for protecting our corpus against
adversarial ML attacks. Our threat researchers also employ advanced techniques
to defend against them. Next, we explore CrowdStrike’s Adversarial Pipeline
tool, one method we use to continually enhance detection coverage.


WILL THE REAL ADVERSARIAL GENERATORS PLEASE STAND UP?

CrowdStrike improves the detection capabilities of its ML models by essentially
red teaming our own classifiers. While dynamic adversarial emulation and other
standard methods to exploit ML are employed as part of red teaming, the
CrowdStrike Adversarial Pipeline stands out as a unique and highly advanced
approach. Designed to be automated and extensible, it allows us to rapidly
integrate different attacks described by our own threat research team, or by
outside open source researchers (the open source community is booming with
methods to evade ML models).

Figure 1. Framework for hardening models against adversarial attacks (click to
enlarge)

The CrowdStrike Adversarial Pipeline’s architecture consists of different
“generators” for the supported file formats with a configurable list of attacks.
For example, generators could add to the malware sample an entire megabyte of
random English words or sections of clean code without impacting its
functionality. These generators work in parallel to provide a flexible tool for
generating adversarial samples that can fit into the classifier. The Adversarial
Pipeline can support fast generation of millions of unique samples, making it an
extremely powerful tool. And, to improve the robustness or stability of ML
classifiers, the generated samples are actually executable binaries that can be
executed on the Falcon sensor. In contrast, other mathematical methods use
perturbation techniques that render sterile adversarial samples.

Figure 2. Examples of adversarial attack generators for various file types
(click to enlarge)


THE BRIGHT SIDE

The situation may seem grim and stacked against protectors, but the good news is
that it’s possible to strengthen each stage of ML model development against
adversarial attacks. CrowdStrike threat researchers employ multiple tactics,
such as cleaning the corpus, deduplication, adding adversarial samples in the
training data and improving feature extraction capabilities. 

In addition, because they recognize that static ML is only one layer of defense,
CrowdStrike threat researchers use ML on behaviors, IOAs and AI-powered IOAs to
provide additional protection layers that in unison are difficult to circumvent.
This comprehensive approach to protection is why the CrowdStrike Falcon platform
continues to lead the industry, including winning the first-ever SE Labs AAA
Advanced Security (Ransomware) Award, achieving 100% ransomware prevention with
zero false positives. 

See for yourself how the industry-leading CrowdStrike Falcon platform protects
against modern threats. Start your 15-day free trial today.


THE VALUE OF GENERATING ADVERSARIAL SAMPLES

Inclusion of new, out-of-sample adversarial samples in ML model training data
has shown an increase of 19% in retention of malware samples at high confidence
levels. It does so while showing little deviation from the original sample
decision value, thus limiting the impact of the attack itself.

In a subset of data selected to closely mirror characteristics of real-world
samples, an experimental detection true positive rate (TPR) of 80% was increased
over the course of several steps to 90% at a fixed false positive rate (FPR)
through the progressive addition of more adversarial samples in the model
training process. This performance improvement was observed not only at a single
FPR but across a wide range. A representation of the results is shown in Figure
3.

Figure 3. True positive rate (TPR) performance improvement through the
progressive addition of more adversarial samples in the model training process
(click to enlarge)


A MINDSET FOR CONTINUOUS RESEARCH

This research highlights the value of the CrowdStrike Adversarial Pipeline’s
ability to generate variations of “new” adversarial samples for enhancing the
detection coverage and efficacy of CrowdStrike’s ML models. Using this tool in
the model training process ensures that our models continuously increase their
effectiveness against adversarial attacks that employ the fully undetectable
malware ML strategy. 

CrowdStrike researchers constantly explore theoretical and applied ML research
to advance and improve detection and efficacy capabilities of our ML models,
setting the industry standard in protecting customers from sophisticated threats
and adversaries to stop breaches.

ADDITIONAL RESOURCES

 * Read about how machine learning is used in cybersecurity.
 * Learn more about the CrowdStrike Falcon platform by visiting the product
   webpage.
 * Test CrowdStrike next-gen AV for yourself. Start your free trial of
   CrowdStrike Falcon® Prevent next-gen antivirus today.

 * Tweet
 * Share



RELATED CONTENT

WELCOME TO THE ADVERSARY UNIVERSE PODCAST: UNMASKING THE THREAT ACTORS TARGETING
YOUR ORGANIZATION




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BEHIND THE CURTAIN: FALCON OVERWATCH HUNTING LEADS EXPLAINED


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   393
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   73
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   190
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   29
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   73
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   20
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   149

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Adversaries as Identity-Focused Attacks Skyrocket
August 8, 2023

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