anomaly detection

Anomaly Detection - SALIL NAVGIRE

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Anomaly Detection

- SALIL NAVGIRE

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

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

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

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

Anomaly detection

techniques

Classification

Nearest Neighbor Clustering Spectral Information

theoretic Statistical Time Series

Anomaly Detection Techniques

• Classification• Neural network based

• Bayesian Network based

• Support Vector Machine based

• Rule based

• Nearest Neighbor• KNN

• Relative density

• Clustering• K means

• SOM

• Statistical• Parametric• Gaussian model based• Regression model based• Mixture of parametric distributions based

• Non-parametric• Histogram based• Kernel function based

• Spectral• Dimensionality reduction

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