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GTC ON-DEMAND

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Abstract:
Well explain how the alerts that a typical security operations center receives are heterogeneous in severity, applicability, and origin. Centers are often overwhelmed and unable to investigate every alert, resulting in missed malicious activity. By leveraging RAPIDS data processing and analytic capabilities, we give analysts insights into these alerts. We also provide high-dimensional co-occurrence, trend identification, and rare event flagging. By reducing the noise floor and extracting additional signals and context from existing alerts, we decrease the time it takes for analysts to triage and investigate alerts. Well share what technologies and pipelines to use and how to integrate them into existing security environments.
Well explain how the alerts that a typical security operations center receives are heterogeneous in severity, applicability, and origin. Centers are often overwhelmed and unable to investigate every alert, resulting in missed malicious activity. By leveraging RAPIDS data processing and analytic capabilities, we give analysts insights into these alerts. We also provide high-dimensional co-occurrence, trend identification, and rare event flagging. By reducing the noise floor and extracting additional signals and context from existing alerts, we decrease the time it takes for analysts to triage and investigate alerts. Well share what technologies and pipelines to use and how to integrate them into existing security environments.  Back
 
Topics:
Cyber Security
Type:
Talk
Event:
GTC Washington D.C.
Year:
2019
Session ID:
DC91356
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Abstract:

Network defense and cybersecurity applications traditionally rely on heuristics and signatures to protect networks and detect anomalies. Large companies may generate over 10TB of data daily, spread across different sensors and heterogenous data types. The difficulty of providing timely ingest, feature engineering, feature exploration, and model generation has made signature-based detection the only option. We'll show how to use RAPIDS and GPU acceleration to overcome these obstacles. We'll walk through data engineering steps involving large amounts of heterogeneous data (both source and format) and explore how GPUs can accelerate feature exploration and hyperparameter selection. This enables more in-house data scientists and information security experts to use internally collected data to generate predictive models for anomaly detection rather than rely on signature-based detection.

Network defense and cybersecurity applications traditionally rely on heuristics and signatures to protect networks and detect anomalies. Large companies may generate over 10TB of data daily, spread across different sensors and heterogenous data types. The difficulty of providing timely ingest, feature engineering, feature exploration, and model generation has made signature-based detection the only option. We'll show how to use RAPIDS and GPU acceleration to overcome these obstacles. We'll walk through data engineering steps involving large amounts of heterogeneous data (both source and format) and explore how GPUs can accelerate feature exploration and hyperparameter selection. This enables more in-house data scientists and information security experts to use internally collected data to generate predictive models for anomaly detection rather than rely on signature-based detection.

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Topics:
Accelerated Data Science, Cyber Security
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9803
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