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Course Outline
Introduction to AI in Cybersecurity
- Overview of AI in threat detection
- AI vs. traditional cybersecurity methods
- Current trends in AI-powered cybersecurity
Machine Learning for Threat Detection
- Supervised and unsupervised learning techniques
- Building predictive models for anomaly detection
- Data preprocessing and feature extraction
Natural Language Processing (NLP) in Cybersecurity
- Using NLP for phishing detection and email analysis
- Text analysis for threat intelligence
- Case studies of NLP applications in cybersecurity
Automating Incident Response with AI
- AI-driven decision-making for incident response
- Building response automation workflows
- Integrating AI with SIEM tools for real-time action
Deep Learning for Advanced Threat Detection
- Neural networks for identifying complex threats
- Implementing deep learning models for malware analysis
- Using AI to combat advanced persistent threats (APTs)
Securing AI Models in Cybersecurity
- Understanding adversarial attacks on AI systems
- Defense strategies for AI-driven security tools
- Ensuring data privacy and model integrity
Integration of AI with Cybersecurity Tools
- Integrating AI into existing cybersecurity frameworks
- AI-based threat intelligence and monitoring
- Optimizing performance of AI-powered tools
Summary and Next Steps
Requirements
- Basic understanding of cybersecurity principles
- Experience with AI and machine learning concepts
- Familiarity with network and system security
Audience
- Cybersecurity professionals
- IT security analysts
- Network administrators
21 Hours
Testimonials (2)
The trainer was very knowledgable and took time to give a very good insight into cyber security issues. A lot of these examples could be used or modified for our learners and create some very engaging lesson activities.
Jenna - Merthyr College
Course - Fundamentals of Corporate Cyber Warfare
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
