Course Outline

Introduction to Cybersecurity and LLMs

  • Current landscape of cybersecurity threats
  • Basics of Large Language Models
  • Advantages of using LLMs in cybersecurity

LLMs for Threat Detection

  • Using LLMs to analyze and interpret security logs
  • Training LLMs for anomaly and pattern detection
  • Case studies: LLMs in intrusion detection systems

LLMs for Security Automation

  • Automating incident response with LLMs
  • LLMs in phishing detection and email filtering
  • Enhancing security protocols with AI

LLMs for Threat Intelligence

  • Gathering and processing threat intelligence with LLMs
  • LLMs for predictive threat modeling
  • Sharing and disseminating intelligence with LLMs

Integrating LLMs into Security Operations

  • Best practices for deploying LLMs in security operations centers
  • Maintaining and updating LLMs for optimal performance
  • Addressing privacy and ethical concerns

Hands-on Lab: Implementing LLMs in Cybersecurity

  • Setting up a cybersecurity lab environment with LLMs
  • Developing a threat detection model using LLMs
  • Simulating attacks and testing model effectiveness

Summary and Next Steps

Requirements

  • An understanding of cybersecurity fundamentals
  • Experience with Python programming
  • Familiarity with machine learning concepts

Audience

  • Cybersecurity professionals
  • Data scientists
  • IT professionals interested in the latest AI-driven security technologies
 14 Hours

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