| Unit name | Anomaly Detection |
|---|---|
| Unit code | MATHM0030 |
| Credit points | 10 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1B (weeks 7 - 12) |
| Unit director | Professor. Rubin-Delanchy |
| Open unit status | Not open |
| Pre-requisites |
Probability 1, Statistics 1 and Statistics 2 (or equivalent) |
| Co-requisites |
None |
| School/department | School of Mathematics |
| Faculty | Faculty of Science |
This unit aims to introduce models of normal network behaviour, anomaly detection, and the process of combining and screening anomalies over space and time.
It will provide the mathematical & statistical underpinnings of anomaly detection for cybersecurity data. It will cover the following topics: dynamic network models, fundamentals of hypothesis testing, combining and screening anomalies, Bayesian methods, Monte-Carlo approaches. In coursework assignments, students will use network, point process and cluster models to find anomalies in real cyber security data.
ILO1: to recognise and apply a range of models for dynamic network data, and their estimation
ILO2: to understand core anomaly detection concepts and tools, including mastering theory and interpretation of hypothesis tests, controlling false positive rates and performing meta-analysis
ILO3: to apply these anomaly detection tools to analyse real large-scale data and report the results
3 lectures per week for 6 weeks, to include 15 hours of new material and 3 hours of problem classes.
Exam (80%) to assess ILOs 1 and 2.
Coursework assignment (20%) spanning the unit to assess ILO 3.
Casella, George, and Roger L. Berger. Statistical inference. Vol. 2. Pacific Grove, CA: Duxbury, 2002.
Daley, D. J., and D. Vere-Jones. An Introduction to the Theory of Point Processes: Volume I: Elementary Theory and Methods, Springer, New York, 2003.
Kolaczyk, E. D. Statistical analysis of network data: Methods and Models. Springer, New York, 2009.
Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. (2nd edition), Springer, New York, 2009.
Heard, Nicholas A., et al. "Bayesian anomaly detection methods for social networks." The Annals of Applied Statistics 4.2 (2010): 645-662.
