Course Outline

AI-Enhanced Threat Detection Techniques

  • Advanced supervised and unsupervised machine learning models
  • Real-time anomaly detection using AI
  • Implementing AI-driven threat hunting techniques

Building Custom AI Models for Cybersecurity

  • Developing models tailored to specific security needs
  • Feature engineering for cybersecurity data
  • Training and validating models with cybersecurity datasets

Incident Response Automation with AI

  • AI-based playbooks for automated response
  • Integrating AI with SOAR platforms for enhanced automation
  • Reducing response time with AI-driven decision-making

Advanced Deep Learning for Cyber Threat Analysis

  • Neural networks for detecting complex malware
  • Using deep learning for advanced persistent threat (APT) detection
  • Case studies on deep learning in threat analysis

Adversarial Machine Learning in Cybersecurity

  • Understanding and defending against adversarial attacks on AI models
  • Implementing robustness techniques for AI security models
  • Securing AI algorithms in dynamic threat landscapes

Integration of AI with Existing Cybersecurity Infrastructure

  • Connecting AI models with SIEM and threat intelligence platforms
  • Optimizing AI performance within cybersecurity workflows
  • Scalable deployment of AI-driven security measures

Threat Intelligence with AI and Big Data

  • Leveraging AI to analyze large-scale threat data
  • Real-time threat intelligence gathering and analysis
  • Using AI to predict and prevent future cyber threats

Summary and Next Steps

Requirements

  • Solid understanding of cybersecurity frameworks and threat detection
  • Experience with machine learning and AI applications in security
  • Familiarity with scripting and automation in security environments

Audience

  • Intermediate to advanced cybersecurity professionals
  • Security operations center (SOC) analysts
  • Threat hunters and incident response teams
 28 Hours

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