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Cybersecurity and Digital Innovation

AI and Machine Learning Applications in Cyber Defense

5 units

Please select a city/session before registration.

About this program

The pace of cybersecurity threats is surpassing the capacity of human analysts to effectively manage them. Artificial Intelligence (AI) and Machine Learning (ML) have become essential tools for protecting systems by automating threat detection, analyzing irregularities, and forecasting emerging risks.
This training course on AI and Machine Learning in Cyber Defense introduces participants to the real-world applications of AI and ML within the cybersecurity domain. Topics include anomaly detection, predictive analytics, behavioral modeling, and AI-enabled incident response.
Through hands-on labs, case studies, and simulation exercises, learners will acquire skills to develop AI-driven defense mechanisms that enhance security operations, reduce response times, and bolster resilience against sophisticated cyberattacks.

Course benefits

  • Gain a clear understanding of AI and ML roles in cyber defense.
  • Enhance capabilities in detecting anomalies and sophisticated threats.
  • Automate cybersecurity monitoring and incident response processes.
  • Leverage predictive analytics to strengthen threat intelligence.
  • Increase efficiency of Security Operations Centers (SOC) and improve cyber resilience.

Key outcomes

  • Examine foundational concepts of AI and ML as applied to cybersecurity.
  • Implement machine learning models for detecting anomalies and intrusions.
  • Integrate artificial intelligence technologies into SOC workflows.
  • Create predictive models to advance cyber threat intelligence.
  • Address ethical considerations and governance issues related to AI in defense.
  • Analyze case studies featuring AI-powered security solutions.
  • Formulate strategies to build AI-driven cyber resilience.

Who should attend

  • Cybersecurity analysts and Security Operations Center (SOC) personnel.
  • Data scientists involved in security-focused roles.
  • IT and network security professionals.
  • Executives aiming to integrate AI into security strategy decisions.

Course outline

1

Unit 1: Overview of AI and ML Applications in Cybersecurity

  • The function of AI and ML in enhancing cyber defense.
  • Advantages and constraints of AI technologies.
  • Real-world examples of AI in security operations.
  • Ethical and regulatory issues surrounding AI use.
2

Unit 2: Cyber Defense Using Machine Learning Techniques

  • Comparison of supervised and unsupervised learning for security purposes.
  • Utilizing classification and clustering methods for intrusion detection.
  • Techniques for feature engineering in cybersecurity datasets.
  • Hands-on lab: creating a basic ML-based intrusion detection system.
3

Unit 3: Behavioral Analytics and Anomaly Detection Methods

  • Identifying irregular activities within network environments.
  • Applying user and entity behavior analytics (UEBA).
  • Machine learning approaches for detecting insider threats.
  • Practical exercise on anomaly detection techniques.
4

Unit 4: Incident Response and SOC Enhancement with AI

  • Implementing automation in SOC workflows.
  • Leveraging AI for log evaluation and integrating with SIEM systems.
  • AI-based strategies for incident response.
  • Simulation exercise: AI-driven SOC automation.
5

Unit 5: Advancements and Prospects of AI in Cybersecurity

  • Utilizing predictive analytics for cyber threat intelligence gathering.
  • The role of deep learning and cutting-edge AI in cyber defense.
  • Challenges posed by post-quantum computing and AI integration.
  • Emerging trends in AI-enhanced security solutions.