& Entity Behavioral Analytics in Incident Detection & Response, 2017"
report has been added to ResearchAndMarkets.com's
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
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.
Key Questions this will Answer
What is the role of UEBA and machine learning in the Incident
Detection & Response (IDR) lifecycle?
How does UEBA uncover threats that are undetectable in signature-based
How algorithms applied to unstructured data are used to augment other
Key Topics Covered:
1. Executive Summary
3. External Challenges - Drivers and Restraints: UEBA Market
4. Machine Learning and Artificial Intelligence (AI)
5. Vendor Analysis of UEBA Platforms in IDR
6. UEBA and Machine Learning in Cybersecurity Platforms
7. The Last Word
8. Vendor Participation Slides
Arctic Wolf Networks
For more information about this report visit https://www.researchandmarkets.com/research/n5nwj9/user_and_entity?w=4
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