Salil presentation 11.07

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    18-Nov-2014
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Transcript of Salil presentation 11.07

  • 1. Anomaly Detection - S A L IL NAVG IR E
  • 2. Introduction problem of finding patterns in data that do not conform to expected behavior covers diverse disciplines from statistics, machine learning, data mining, information theory, spectral theory
  • 3. Applications Intrusion detection- detection of malicious activity Host based OS call traces Network based packet level traces Fraud detection - detection of criminal activities in commercial organizations Credit card fraud detection Insurance Claim Fraud Detection Insider trading detection Industrial damage detection Anomaly detection in data Anomaly detection in sensor networks
  • 4. Challenges Defining normal region Sometimes malicious agent adapt themselves to appear as normal observation Different techniques for different application domain Availability of labeled data for training Sometimes noise is similar to anomaly and difficult to distinguish
  • 5. Different aspects of detection techniques Nature of input data Types of Anomaly Point Anomalies Contextual Anomalies Collective Anomalies Data Labels Supervised anomaly detection Semi-Supervised anomaly detection Unsupervised anomaly detection Output Scores Labels
  • 6. Anomaly Detection Techniques Anomaly detection techniques Classification Nearest Neighbor Clustering Spectral Information theoretic Statistical Time Series
  • 7. Classification Neural network based Bayesian Network based Support Vector Machine based Rule based Nearest Neighbor KNN Relative density Clustering K means SOM
  • 8. Statistical Parametric Gaussian model based Regression model based Mixture of parametric distributions based Non-parametric Histogram based Kernel function based Spectral Dimensionality reduction