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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