Machine Learning for Incident Response (CYBER PRO TRAIN IT –ES -GR)

This course covers how machine learning models can be used to automate and enhance incident response processes, including the analysis of security logs, automated threat hunting, and the development of self-learning security systems Machine Learning for SIEM Solutions. It also explores the integration of machine learning techniques into Security Information and Event Management (SIEM) solutions. 

The program covers key topics such as security data analysis, event correlation, and security automation. Through theoretical and practical lessons, participants will develop skills in implementing and manage effective and innovative SIEM solutions.

About the course

The course is structured into five modules, each with specific objectives:

Module 1 - 30 min: Introduction and Traditional Incident Response

• Objectives: Establish the foundation (standard IR process) before introducing ML innovation
• The organizational process for managing the consequences of a security breach or cyberattack, minimizing damage and restoration time.
• Preparation, Detection and Analysis, Containment, Eradication and Recovery, Lessons Learned (Post-Incident).
• Interactive Q&A

Module 2 – 1 hour: Why ML for Incident Response?

• Objectives: Justify the adoption of ML with clear benefits and data
• The ML Revolution.
• ML automates the analysis of Big Data volumes unmanageable by humans.
• It drastically reduces false positives, accelerates detection (MTTD), and allows for proactive rather than reactive response.
• Interactive Q&A

Module 3 – 1 hour: Applications and Use Cases

• Objectives: Illustrate the fundamental use of ML on raw data (logs).
• ML excels at analyzing billions of log events from diverse sources to find the "signal in the noise."
• Anomaly Detection (Unsupervised): Identifies deviations from "normal" network and user behavior (UEBA - User and Entity Behavior Analytics).
• Classification (Supervised): Assigns labels (e.g., "Malware," "Phishing," "Normal") to new events based on historical attack data.
• Interactive Q&A

Module 4 – 45 min: Threat Hunting

• Objectives: Explain how ML supports active threat hunting.
• Proactive Threat Search.
• ML proactively searches for complex attack patterns (TTPs) hidden in the data.
• Clustering: Groups seemingly unrelated events (e.g., a failed login, a rare file access, an external connection) to reveal a coordinated attack.
• Interactive Q&A

Module 5 - 45 min: Self-Learning Systems

• Objectives: Describe the concept of "intelligent" and self-improving security.
• Continuous Adaptation: ML models update in real-time with new data, improving accuracy and adapting to zero-day threats.
• Dynamic Baseline: Network "normality" is constantly redefined, improving anomaly detection precision (UEBA).
• Automatic Response: SOAR + ML systems can automatically isolate an infected endpoint without human intervention.
• Interactive Q&A

Final interactive discussions.

When does it take place?

· 20 OCT 2026 – Italian (09:00 - 13:00) (GMT+1)

· 21 OCT 2026 – Greek (09:00 – 13:00) (GMT+2)

· 22 OCT 2026 – Spanish (09:00 - 13:00) (GMT+1)

The course is designed for professionals, including SME employees and Public Administration staff, looking to enhance their cybersecurity knowledge. Each module combines a theoretical explanation of the main concepts with an interactive quiz to reinforce learning. The training materials include PowerPoint slides, interactive quizzes, and a final assessment. Moreover, there are additional resources for individual study.