AI for Cyber Security_Trí tuệ nhân tạo cho An ninh mạng

This course provides foundational knowledge of machine learning (ML) and AI techniques for solving cybersecurity problems. The content focuses on modeling security challenges as machine learning problems, processing and representing cybersecurity data (system logs, network traffic, security events, endpoint telemetry), and building and evaluating models. This ultimately supports the tracking, monitoring, detection, and prevention of cyber threats to protect data and systems. The module combines theoretical foundations (basic concepts of supervised/unsupervised learning, classification, regression, anomaly detection, model evaluation, overfitting, and ML pipelines) with hands-on Python labs using popular libraries in the cybersecurity domain (such as scikit-learn and introductory deep learning modules). Students will work with real-world cybersecurity datasets applied to tasks such as Intrusion Detection Systems (IDS), malware classification, phishing URL/email detection, and user/system behavior anomaly detection. Upon completion of this course, students will be able to: 1. Understand and explain the fundamental concepts of machine learning and how to map them to cybersecurity problems. 2. Design and deploy simple, end-to-end machine learning pipelines: data collection, preprocessing, feature extraction/engineering, training, evaluation, and model improvement for cybersecurity tasks. 3. Apply machine learning techniques to predict attack behaviors, detect vulnerabilities/anomalous activities, and support decision-making in secure system operations. 4. Build a solid foundation for advanced courses in malware analysis, threat hunting, SOC engineering, and AI system security, while gaining practical experience in developing machine learning solutions for digital security.