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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 (3)
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
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
