Data-Driven Network Security Essentials

  • 1-2 hours worth of material
  • LinkedIn Learning
  • English
Data-Driven Network Security Essentials

Course Overview

Learn how to improve network security by leveraging data. Learn about data collection, network forensics, and how to use machine learning and visualization to process network data.

Course Circullum

Introduction
  • Welcome
  • What you should know
1. Network Security Review
  • Network security
  • Firewalls
  • VPNs
  • Intrusion detection and prevention systems
  • Vulnerability management systems and security information and event management (SIEM)
2. Network Data Sources
  • Use network data to improve security
  • Packet Capture
  • Firewall logs
  • IDS and IPS data
  • Vulnerability management system and SIEM data
  • Application data
  • Operating system (OS) data
3. Data Collection
  • Use log servers to collect data
  • Collect packet sniffer data
  • Collect IDS and IPS data
  • Collect vulnerability management system and SIEM data
  • Collect application data
  • Collect OS data
4. Data Analytics
  • Machine learning to process network data
  • Machine learning to detect a network anomaly
  • Azure machine learning service
  • Detect network anomalies using the Azure machine learning service
5. Forensics
  • Network forensics
  • Use data science to conduct a network forensics investigation
6. Visualization
  • Network security visualization
  • Visualization targets
  • Visualization steps
  • Use data visualization tools
  • Learn by example
Conclusion
  • Next steps
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This Course Include:
Introduction
  • Welcome
  • What you should know
1. Network Security Review
  • Network security
  • Firewalls
  • VPNs
  • Intrusion detection and prevention systems
  • Vulnerability management systems and security information and event management (SIEM)
2. Network Data Sources
  • Use network data to improve security
  • Packet Capture
  • Firewall logs
  • IDS and IPS data
  • Vulnerability management system and SIEM data
  • Application data
  • Operating system (OS) data
3. Data Collection
  • Use log servers to collect data
  • Collect packet sniffer data
  • Collect IDS and IPS data
  • Collect vulnerability management system and SIEM data
  • Collect application data
  • Collect OS data
4. Data Analytics
  • Machine learning to process network data
  • Machine learning to detect a network anomaly
  • Azure machine learning service
  • Detect network anomalies using the Azure machine learning service
5. Forensics
  • Network forensics
  • Use data science to conduct a network forensics investigation
6. Visualization
  • Network security visualization
  • Visualization targets
  • Visualization steps
  • Use data visualization tools
  • Learn by example
Conclusion
  • Next steps
  • Provider:LinkedIn Learning
  • Certificate:Certificate Available
  • Language:English
  • Duration:1-2 hours worth of material
  • Language CC:

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