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GTC ON-DEMAND

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Abstract:
To counter evolving threats, current Security Operation Centers (SOCs) collect huge amounts of data from a range of sensors and endpoints. Theyre responsible for triaging more data and responding to more events than past generations of SOCs. Additional sensing and collection provides more visibility into network environments, but also requires SOCs to pivot quickly across heterogeneous data sources and respond to threats while still providing a familiar interface and capability set to analysts, threat hunters, and forensic investigators. Well demonstrate how to seamlessly achieve fast and customizable capabilities by extending the security information and event management system with RAPIDS and RAPIDS-enabled workflows. Well discuss the integration of RAPIDS into the SOC environment and how it accelerates detection and response.
To counter evolving threats, current Security Operation Centers (SOCs) collect huge amounts of data from a range of sensors and endpoints. Theyre responsible for triaging more data and responding to more events than past generations of SOCs. Additional sensing and collection provides more visibility into network environments, but also requires SOCs to pivot quickly across heterogeneous data sources and respond to threats while still providing a familiar interface and capability set to analysts, threat hunters, and forensic investigators. Well demonstrate how to seamlessly achieve fast and customizable capabilities by extending the security information and event management system with RAPIDS and RAPIDS-enabled workflows. Well discuss the integration of RAPIDS into the SOC environment and how it accelerates detection and response.  Back
 
Topics:
Cyber Security
Type:
Talk
Event:
GTC Washington D.C.
Year:
2019
Session ID:
DC91355
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Abstract:
Traditional means of network mapping rely on expert knowledge, well-curated databases of network assets, and active internal scanning. Network maps are frequently out of date and often unable to provide the necessary ground-truth data to IT and security. We'll show how to leverage RAPIDS and GPU-Accelerated data science to learn a network mapping from passively generated logs. We'll discuss how we take this a step further by applying multiple machine learning analytics to the graph to infer asset ownership, classify assets and services on the network, and provide near real-time updates and alerts based on changes to the network topology. We'll explain how near real-time ingest and processing capabilities allow us to visualize the network quickly and provide context to the security professional in a timely manner.
Traditional means of network mapping rely on expert knowledge, well-curated databases of network assets, and active internal scanning. Network maps are frequently out of date and often unable to provide the necessary ground-truth data to IT and security. We'll show how to leverage RAPIDS and GPU-Accelerated data science to learn a network mapping from passively generated logs. We'll discuss how we take this a step further by applying multiple machine learning analytics to the graph to infer asset ownership, classify assets and services on the network, and provide near real-time updates and alerts based on changes to the network topology. We'll explain how near real-time ingest and processing capabilities allow us to visualize the network quickly and provide context to the security professional in a timely manner.  Back
 
Topics:
Accelerated Data Science, Cyber Security
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9802
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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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Abstract:

Rules-based approaches to cyber security detection do not scale and are burdened by a reliance on human engineering. In this session, we explore machine learning approaches to cyber security threats, specifically those related to failed login attempts (often a left-of-compromise indicator of an attack) and credential misuse (abnormal behavior). Rather than apply rules, we use the data processing and analytic capabilities of the GPU Open Analytics Initiative (GOAI) to accelerate model training, inference, and other steps necessary to provide actionable alerts to an analyst in near real-time.

Rules-based approaches to cyber security detection do not scale and are burdened by a reliance on human engineering. In this session, we explore machine learning approaches to cyber security threats, specifically those related to failed login attempts (often a left-of-compromise indicator of an attack) and credential misuse (abnormal behavior). Rather than apply rules, we use the data processing and analytic capabilities of the GPU Open Analytics Initiative (GOAI) to accelerate model training, inference, and other steps necessary to provide actionable alerts to an analyst in near real-time.

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Topics:
Cyber Security, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Washington D.C.
Year:
2018
Session ID:
DC8190
Streaming:
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