Anomaly Detection using Deep Auto-Encoders
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This document discusses anomaly detection using deep auto-encoders. It begins by defining outliers and anomalies, and describes challenges with traditional machine learning techniques for anomaly detection. It then introduces hierarchical feature learning using deep neural networks, specifically using auto-encoders to learn the structure of normal data and detect anomalies based on reconstruction error. Examples of applying this for ECG pulse detection and MNIST digit recognition are provided.
Anomaly Detection using Deep Auto-Encoders
- 1. ANOMALY DETECTION USING DEEP AUTO-ENCODERS Gianmario Spacagna
- 2. What you will (briefly) learn – What is an anomaly (and an outlier) – Popular techniques used in shallow machine learning – Why deep learning can make the difference – Anomaly detection using deep auto—encoders – H2O overview – Code examples in Jupyter: ■ ECG pulse detection ■ MNIST digit recognition (optional)
- 3. 1. Machine Learning – An Introduction 2. Neural Networks 3. Deep Learning Fundamentals 4. Unsupervised Feature Learning 5. Image Recognition 6. Recurrent Neural Networks and Languages Models 7. Deep Learning for Board Games 8. Deep Learning for Computer Games 9. Anomaly Detection 10. Building a Production-ready Intrusion Detection System
- 4. Why this use case? ■ Anomaly detection is crucial to many business applications ■ Smart feature representation => better anomaly detection ■ Deep Learning works very well on learning relationships in the underlying raw data (will see how…)
- 5. Outlier vs Anomaly “An outlier is a legitimate data point that’s far away from the mean or median in a distribution. It may be unusual, like a 9.6-second 100-meter dash, but still within the realm of reality. An anomaly is an illegitimate data point that’s generated by a different process than whatever generated the rest of the data.” Ravi Parikh http://data.heapanalytics.com/garbage-in-garbage-out-how-anomalies- can-wreck-your-data
- 6. Data modeling ■ Point anomaly (e.g. black sheep) ■ Contextual anomaly (e.g. selling ice-creams in January) ■ Collective anomaly (e.g. sequence of suspected credit card activities)
- 7. Detection modeling (and its limitations) ■ Supervised (classification) – Data skewness, lack of counter examples ■ Unsupervised (clustering) – Curse of dimensionality ■ Semi-supervised (novelty detection) – Require a “normal” training dataset
- 8. Real world applications ■ Manufacturing => hardware faults ■ Law-enforcement => reveal criminal activities ■ Network system => detect intrusions or anomalous behaviors ■ Internet Security => malware detection ■ Financial services => frauds ■ Marketing / business strategy => spotting profitable customers ■ Healthcare => Medical diagnosis
- 9. What’s the challenge? “Coming up with features is difficult, time-consuming, requires expert knowledge. When working applications of learning, we spend a lot of time tuning features.“ Andrew Ng, Machine Learning and AI via Brain simulations, Stanford University
- 10. Hierarchical Feature Learning NVIDIA Deep Learning Course: Class #1 – Introduction to Deep Learning https://www.youtube.com/watch?v=6eBpjEdgSm0
- 11. Structural representation Advanced Topics, http://slideplayer.com/slide/3471890/
- 12. Signal propagation Schematic diagram of back-propagation neural networks with two hidden layers. Factor selection for delay analysis using Knowledge Discovery in Databases
- 13. Auto-encoders • Signal propagation output: approximate an identity function • Error back propagation: Mean Squared Error MSE (*) between the original datum and the reconstructed one (*) in case of numerical data
- 14. Novelty detection using auto-encoders 1. Identify a training dataset of what is considered “normal” 2. Learn what “normal” means aka. learn the structures of normal behavior 3. Try to reconstruct never-seen points re-using the same structure, if the error is high means the point deviates from the normal distribution TRAIN Auto-Encoder RECONSTRUCT RECONSTRUCT Low error High error
- 15. Features compression ■ Use just the encoder to compress data into a reduced dimensional space then use traditional unsupervised learning Tom Mitchell’s example of an auto-encoder: You can represent any combination of the 8 binary inputs using only 3 decimal values
- 17. Examples ■ ECG Anomaly Pulse Detection ■ MNIST Anomaly Digit Recognition (Optional) ■ Jupyter notebooks available on https://github.com/packtmayur/Python-Deep-Learning/tree/master/chapter_9
- 18. Summary ■ We listed a few real-world applications of anomaly detection ■ We covered some of the most popular techniques in the literature with their limitations ■ We proposed an overview of how deep neural networks work and why they are great for learning smart feature representations ■ We proposed 2 semi-supervised approaches using deep auto-encoders: – Novel detection – Feature compression
- 19. Going deeper ■ Advanced modeling: – Denoising auto-encoders – Contractive auto-encoders – Sparse auto-encoders – Variational auto-encoders (for better novelty detection) – Stacked auto-encoders (for better feature compression) ■ Building a production-ready intrusion detection system – Validating and testing with labels and in absence of ground truth – Evaluation KPIs for anomaly detection – A/B(C/D) testing
- 20. "Data scientists realize that their best days coincide with discovery of truly odd features in the data." Haystacks and Needles: Anomaly Detection By: Gerhard Pilcher & Kenny Darrell, Data Mining Analyst, Elder Research, Inc.