Anomaly Detection using Machine Learning Techniques

Machine learning algorithms enable the systems to observe the behaviour based on real data. Based on the past experiences algorithms can be designed which allow computers to display behavior learned from past experiences. Machine learning algorithms are used to analyze the abnormal instances in a particular network. The algorithms can be trained for multiple data and can track the exploitation of a network. This idea is used for fraud detection and monitoring of the machines. Supervised learning is essential in terms of training and analyzing the abnormal behavior in a network. This paper presents the supervised techniques used to detect the network anomalies.