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Salil presentation 11.07

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  • 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