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COMP9071 - Fraud and Anomaly Detection

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Title:Fraud and Anomaly Detection
Long Title:Fraud and Anomaly Detection
Module Code:COMP9071
  
Duration:1 Semester
Credits: 5
NFQ Level:Expert
Field of Study: Computer Science
Valid From: Semester 1 - 2018/19 ( September 2018 )
Module Delivered in 6 programme(s)
Module Coordinator: Sean McSweeney
Module Author: Samane Abdi
Module Description: Anomaly detection is the process of identifying unusual patterns of events, observations, or a set of data which do not conform to an expected normal behaviour. This module will provide learners with a comprehensive introduction to the theory underpinning anomaly detection and will also equip learners with the knowledge to effectively apply a range of anomaly detection techniques (such as clustering and rule-based algorithms) to real-world problems such as fraud detection.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Apply statistical algorithms to anomaly detection for a specific application domain.
LO2 Compare the performance of a range of classification-based machine learning algorithms to anomaly detection problems.
LO3 Implement a clustering based anomaly detection.
LO4 Develop an online model for anomaly detection over big-data streams.
Pre-requisite learning
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
Co-requisites
No Co Requisites listed
 

Module Content & Assessment

Indicative Content
Statistical Techniques
Overview and application of a range of parametric and non-parametric statistical techniques for anomaly detection such as change point detection, Gaussian mixture models and hidden Markov models.
Classification Models
Anomaly detection using a range of relevant machine learning classification techniques such as neural networks, SVMs, rule-based algorithms, ensembles techniques, distance-based and density-based algorithms.
Unsupervised Model and Evaluation
Application of unsupervised models to anomaly detection problems such as LOF, COF, LOCI and CBLOF. The role of dimensionality reduction techniques such as PCA and feature selection. Best practice evaluation techniques such as F1 scores and ROC curves.
Anomaly Detection at Scale
Implement and deploy a model for real-time anomaly detection in a big data environment using Spark Streaming and MLlib for an application domain.
Assessment Breakdown%
Course Work100.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project Perform a comparative analysis of a range of statistical techniques versus classification models to detect anomalies. Standard methodologies should be applied and the performance should be comprehensively evaluated. 1,2 50.0 Week 7
Project By employing appropriate research methods, the student is expected to apply the unsupervised techniques to Implement and deploy a model for real-time anomaly detection in a big data environment. 3,4 50.0 Week 13
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The institute reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Delivers the concepts and theories underpinning the learning outcomes. 2.0 Every Week 2.00
Lab Application of learning to case studies and project work. 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Student undertakes independent study. The student reads recommended papers and practices implementation. 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Delivers the concepts and theories underpinning the learning outcomes. 2.0 Every Week 2.00
Lab Application of learning to case studies and project work. 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Student undertakes independent study. The student reads recommended papers and practices implementation. 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Sumeet Dua, Xian Du 2011, Data Mining and Machine Learning in Cybersecurity, Auerbach Publications [ISBN: 978-143983942]
Supplementary Book Resources
  • Ted Dunning, Ellen Friedman 2013, Practical Machine Learning: A New Look at Anomaly Detection, 1 Ed., O'Reilly Media [ISBN: 978-149191160]
Recommended Article/Paper Resources
  • ACM Digital Library 2009, Anomaly detection: A survey, ACM Computing Surveys (CSUR)
Other Resources
 

Module Delivered in

Programme Code Programme Semester Delivery
CR_KCLDC_9 Master of Science in Cloud Computing 1 Elective
CR_KCLDC_9 Master of Science in Cloud Computing 2 Elective
CR_KINSE_9 Master of Science in Cybersecurity 2 Elective
CR_KCYMN_9 Master of Science in Cybersecurity Management 2 Group Elective 2
CR_KINSY_9 Postgraduate Diploma in Science in Cybersecurity 2 Elective
CR_KCYMT_9 Postgraduate Diploma in Science in Cybersecurity Management 2 Group Elective 2

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