ITKST47 Advanced Anomaly Detection: Theory, Algorithms and Applications (5 cr)

Sisältö

In the first course (ITKST42), we introduced the concept of anomalies, provided motivation for anomaly detection and explored several real-world use cases of anomalies.

We overviewed different data types, including high-dimensional data, and provided methods for pre-processing of data. We explored the different categories of anomaly detection and the different types of anomalies and presented methods for evaluation of anomaly detection methods.

We surveyed nearest neighbors based techniques and clustering based techniques techniques for anomaly detection and explored the theory behind each technique, its different categories, the pros and cons, demos and practice.

In this course (ITKST47), we will survey different techniques for advanced anomaly detection. For example, classification based techniques, statistical based techniques and spectral based techniques.

For each technique, we will explore the theory behind it, its different categories, the pros and cons, demos and practice.

We will have two mandatory assignments. The final assignment will be a Cyber Ware Game competition, where the students will implement the learnt methods to detect viruses and malwares.

Suoritustavat

We will have two mandatory assignments.

A mid-course assignment (25% of the final grade)

A final assignment (75% of the final grade)

Arviointiperusteet

A mid-course assignment (25% of the final grade)
A final assignment (75% of the final grade)