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Time series anomaly detection using cnn coupled with data augmentation using ga ns

Time Series Anomaly detection on structured data from IOT Network using CNN

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Time series anomaly detection using cnn coupled with data augmentation using ga ns

  1. 1. Time Series Anomaly detection on structured data from IOT Network using CNN using synthetic labelled data generation using GANs
  2. 2. PROBLEM DESCRIPTION Standardized data sets have been a crucial factor in the success of ML, however, there is nothing like that available for IIOT domains. Many/most data sets are toy, noisy, unnormalized; some data sets are proprietary. Manual analysis of massive amount of data and associated metrics is inefficient and practically not feasible. Manual analysis is also not sustainable as this method is subject to individual personnel knowledge and experience which can result in inconsistencies and rendering the process non-scalable.
  3. 3. BRIEF SUMMARY OF THE PROPOSED SOLUTION • The proposed methodology consists of several components to classify various types of anomalies that can occur in an IIOT network type with more emphasis given to generating synthetic data using deep generative model (DCGAN) to achieve high generalizability of the anomaly detection classification model.
  4. 4. BRIEF SUMMARY OF THE PROPOSED SOLUTION Stage 1 - The input dataset is pre-processed. - Performance metrics are first encoded and then transformed into an innovative multi- dimensional representation of various performance metrics and time to capture the multi-spatial relationships between them. Stage 2 - Deep Convolutional Generative Adversarial Networks (DCGAN) model which is used to generate synthetic data for each anomaly class enabled by the innovative image representation proposed in stage 1. - We also propose multiple evaluation metrics to evaluate deep generative models on diversity of generated data and closeness to the target distribution. - Additionally, we performed sampling saturation checks for the trained generative model without compromising on the evaluation metrics. Stage 3 - We merge synthetic data and real data. This merged data is used to train a classifier model. - We propose multiple evaluation metrics to evaluate CNN model which quantifies the improvement made by synthetic images generated by deep generative models.
  5. 5. Model Evaluation Framework  EvalDiversity learns a classifier on DCGAN generated synthetic data and measures the performance on real data(image representation). This evaluates the diversity and realism of generated synthetic images.  EvalDistributionAccuracy learns a classifier on real data (image representation) and evaluates it on generated synthetic images. This measures how close is the generated data distribution to the actual data distribution.  EvalMergedModelTestMergedData learns a classifier on a merged data set (real + synthetic data) and evaluated on merged data. This further certifies the diversity of the images generated by the deep generative model.  EvalMergedModelTestRealData learns a classifier on a merged data set comprising of 50% of real data and 50% of generated data. Evaluation is done only on real data not used for training. This evaluates whether adding generated data improves the classifier trained on original data.
  6. 6. Comparison – Generated vs Real Data Class 1
  7. 7. Comparison – Generated vs Real Data Class 2
  8. 8. Comparison – Generated vs Real Data Class 3
  9. 9. Loss – Generator vs Discriminator
  10. 10. Evaluation Classifier trained on real images EvalDiversity EvalDistributionAccuracy
  11. 11. Evaluation EvalMergedModelTestMergedData EvalMergedModelTestRealData

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