Course 2019-2020 a.y.

This course introduces students to relevant artificial intelligence concepts and the data analysis methods and technologies employed in security related applications. The contents include:

• The cyber threat landscape
• Artificial intelligence and machine learning
• Uses and limitations of machine learning for security
• Popular classification algorithms (logistic regression, decision trees, etc.)
• The training of classifiers (data preparation, feature selection, overfitting, evaluation)
• Clustering and anomaly detection techniques
• Practical investigation of malware analysis, network security and abuse detection
• Advanced concepts: building production systems, and adversarial machine learning
• Open source intelligence tools

• Discuss the cyber security threat landscape.
• Describe the artificial intelligence techniques commonly used in cyber security applications, such as classification, clustering and anomaly detection techniques.
• Explain the capabilities and limitations of machine learning techniques for security applications including the types of threats that can and cannot be handled by them.
• Discuss practical aspects of training and testing threat detection systems.
• Reason about future security threats that could result from adversarial machine learning techniques.

• Decide which artificial intelligence tools are appropriate for dealing with certain security threats to organisations and reason about the level of protection afforded by the tools.
• Apply classification technology to develop a threat detection system by collecting appropriate training data and training appropriate classifiers.
• Apply clustering and anomaly detection techniques to identify anomalous behavior in log data.

• Face-to-face lectures
• Exercises (exercises, database, software etc.)
• Individual assignments

Attendance

Theoretical discussions and practical (programming) exercises will be performed in class, making attendance critical for achieving the desired learning outcomes.

Assessment for attending students will consist of:

1. Individual assignments (25% of the final grade)
2. Written exam (75% of final grade)

1. Individual assignments (25% of the final grade, submitted by email)
2. Written exam (75% of final grade)

• Teaching notes, suggested readings
• Textbook: Clarence Chio & David Freeman, “Machine Learning & Security: Protecting Systems with Data and Algorithms”,  O’Reilly.

• Teaching notes, suggested readings
• Textbook: Clarence Chio & David Freeman, “Machine Learning & Security: Protecting Systems with Data and Algorithms”,  O’Reilly.