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Course Outline

Introduction to Cybersecurity and LLMs

  • Overview of the current cybersecurity threat landscape.
  • Fundamentals of Large Language Models.
  • Key benefits of utilising LLMs in cybersecurity.

LLMs for Threat Detection

  • Leveraging LLMs to analyse and interpret security logs.
  • Training LLMs to detect anomalies and patterns.
  • Case studies: Application of LLMs in intrusion detection systems.

LLMs for Security Automation

  • Automating incident response processes using LLMs.
  • Utilising LLMs for phishing detection and email filtering.
  • Enhancing security protocols through AI.

LLMs for Threat Intelligence

  • Collecting and processing threat intelligence with LLMs.
  • Employing LLMs for predictive threat modelling.
  • Sharing and disseminating intelligence via LLMs.

Integrating LLMs into Security Operations

  • Best practices for deploying LLMs in Security Operations Centres (SOCs).
  • Maintaining and updating LLMs for optimal performance.
  • Addressing privacy and ethical considerations.

Hands-on Lab: Implementing LLMs in Cybersecurity

  • Setting up a cybersecurity lab environment equipped with LLMs.
  • Developing a threat detection model using LLMs.
  • Simulating attacks to test the model’s effectiveness.

Summary and Next Steps

Requirements

  • Foundational knowledge of cybersecurity principles.
  • Proficiency in Python programming.
  • Familiarity with core machine learning concepts.

Target Audience

  • Cybersecurity specialists.
  • Data scientists.
  • IT professionals eager to explore the latest AI-driven security technologies.
 14 Hours

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