An anomaly is abnormal activity or deviation from the normal behaviour. Anomaly detection is the process of removing these abnormal or anomalous behaviours from the data or services. Anomaly detection techniques are used to detect and discard anomalies from the data or services. In this survey paper we describe overview of some anomaly detection techniques which are on collective anomaly detection and clustering anomaly which are generated due to variety of abnormal activities such as credit card fraud detection, mobile phone fraud, banking fraud, cyber attack etc. an important aspect as the nature of anomaly. In existing paper introduced the concept of collective anomaly for network traffic analysis. It’s used the variant of k-mean and x-mean algorithm for clustering network traffic and detects DOS attack. In the anomaly detection models anomalies are detected by comparing the tracing data with the actual data. On the basis of comparison deviations in the traced data or services are identified and they are considered as anomaly. To overcome these entire problems we proposed a novel technique to the combination of classification and Genetic based anomaly. We develop an efficient sampling technique which capture the underlying distribution of data and create a summary to be able to monitor high capacity network.

Anomaly Detection Techniques Clustering CAD Genetic and Classification based Technique