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Anomaly detection in deep learning


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Brief presentation on anomaly detection with deep learning.

Published in: Data & Analytics

Anomaly detection in deep learning

  1. 1. Anomaly Detection in Deep Learning Adam Gibson Skymind - Reactive Meetup 2016 @ Google Tokyo
  2. 2. What’s an “Anomaly?” ● Abnormal Patterns in Data ● Fraud Detection - “Bad credit card Transactions” ● ALSO Fraud detection - Detecting fake locations with call detail records ● Network Intrusion - Abnormal Activity in a network ● Broken Computers in a data center
  3. 3. Brief Case Studies - eg: Why am I up here? ● Telco: learning-software-to-identify-fraud/ ● Network Infrastructure: https://insights.ubuntu. com/2016/04/25/making-deep-learning-accessible-on- openstack/
  4. 4. Network Infra - Save time and Money avoiding Broken workloads by auto migration before it happens
  5. 5. Why Deep Learning? ● Learns well from lots of data ● Own feature representation: Robust to noise and allows for learning cross domain patterns ● Already applied in ads: Google itself invests lots in this same kind of pattern recognition (targeting/relevance)
  6. 6. Techniques ● Unsupervised - Use autoencoder reconstruction error and use moving averages use dropout with a set time window ● Supervised - RNNs Learn from a set of yes/nos in a time series. RNNs can learn from a series of time steps and predict when an anomaly is about to occur. ● Use streaming/minibatches (all neural nets can learn like this)
  7. 7. Some definitions ● Reconstruction Error: Autoencoders can learn from unsupervised pretraining and learn how to reconstruct data. Minimize KL Divergence (the delta between two probability distributions ● RNN/Time Series: See
  8. 8. Production ● Kafka/Spark Streaming/Flink/Apex ● Neural net works as consumer of streaming updates ● Data? Mostly log ingestion, could be video
  9. 9. Questions? Email: Twitter: agibsonccc Github: agibsonccc
  10. 10. Upcoming talks Hadoop Summit: San Jose