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User & Entity Behavioral Analytics in Incident Detection & Response, 2017

User & Entity Behavioral Analytics in Incident Detection & Response, 2017

The report is about User and Entity Behavioral Analytics (UEBA) platforms used in the Incident Detection and Response (IDR) lifecycle and machine learning in various procedures in cybersecurity technologies. UEBA platforms apply algorithms over unstructured data sets to locate anomalies. By using a algorithm-based approach, UEBA is not limited to what can be learned from signatures or from techniques that require packet parsing. Divorced from signatures and packets, UEBA platforms are positioned to detect threats not possible in traditional cyber defense tools. UEBA platforms are deployed (typically) as plug-ins to network ingress/egress points and do not require agents or sensors (although additional visibility and endpoint management with the deployments of agents could be gained).If a UEBA platform is trusted, it can reduce agent management, and more importantly, reduce the number of alerts facing SOC analysts.

Research Highlights
The report is about User and Entity Behavioral Analytics (UEBA) platforms used in the Incident Detection and Response (IDR) lifecycle and machine learning in various procedures in cybersecurity technologies.

UEBA platforms are attractive on several levels:
UEBA platforms apply algorithms over unstructured data sets to look for anomalies.
By using a math-based approach, UEBA is not limited to what can be learned from signatures or from techniques that require packet parsing.
Divorced from signatures and packets, UEBA platforms may be able to detect threats not possible in traditional cyber defense tools.
UEBA platforms are deployed (typically) as plug-ins to network ingress/egress points and do not require agents or sensors (although additional visibility and endpoint management with the deployments of agents could be gained).
If a UEBA platform is trusted, it can reduce lightweight agent management, and more importantly, reduce the number of alerts facing SOC analysts.


  • Executive Summary
    • Key Findings
    • Executive Summary-Key Questions This Study Will Answer
  • Introduction
    • Introduction to the Research
    • Definitions UEBA, Machine Learning, and Artificial Intelligence
  • External Challenges-Drivers and Restraints: UEBA Market
    • Drivers and Restraints
    • Drivers Explained
    • Restraints Explained
  • Machine Learning and Artificial Intelligence (AI)
    • Machine Learning and Artificial Intelligence Role in IDR
    • More about Machine Learning and Artificial Intelligence
  • Vendor Analysis of UEBA Platforms in IDR
    • Attributes of Vendor Analysis of UEBA Platforms in IDR
    • Vendor Analysis of UEBA Platforms in IDR
      • Table DarkLight Cyber Effects Matrix Dashboard
  • UEBA and Machine Learning in Cybersecurity Platforms
    • UEBA and Machine Learning in Cybersecurity Platforms
    • UEBA and Machine Learning in Cybersecurity Platforms-LogRhythm
    • UEBA and Machine Learning in Cybersecurity Platforms
  • The Last Word
    • The Last Word-Predictions
    • The Last Word-Recommendations
  • Vendor Participation Slides
    • Vendor Profile-Arctic Wolf Networks
    • Vendor Profile-Aruba, a Hewlett Packard Enterprise Company
    • Vendor Profile-Awake Security
    • Vendor Profile-Darktrace The Enterprise Immune System
    • Vendor Profile-Darktrace Autonomous Response Capability: Antigena
    • Vendor Profile-Demisto
    • Vendor Profile-Exabeam
    • Vendor Profile-Lacework
    • Vendor Profile-Lastline
    • Vendor Profile-LogRhythm Threat Lifecycle Management
    • Vendor Profile-LogRhythm
    • Vendor Profile-Lumeta
    • Vendor Profile-SecBI
    • Vendor Profile-ThetaRay
  • Appendix
    • Appendix A-What are the Criteria in Multifactor Incident Detection and Response (IDR)
    • Appendix A-Cybersecurity Technology Classes Included in Multifactor IDR
    • Appendix A-Cybersecurity Technology Classes Not Included in Multifactor IDR
    • Appendix B-Explaining Individual Attributes of the IDR Lifecycle
    • Methodology

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