Network Anomaly Detection and User Behavior Analysis using Machine Learning

Machine Learning Approaches to Network Anomaly Detection

Networks of various kinds often experience anomalous behaviour. Examples include attacks or large data transfers in IP networks, presence of intruders in distributed video surveillance systems, and an automobile accident or an untimely congestion in a road network. Machine learning techniques enable the development of anomaly detection algorithms that are non-parametric, adaptive to changes in ...

Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms

Anomaly detection is the practice of identifying items or events that do not conform to an expected behavior or do not correlate with other items in a dataset. It has previously been applied to areas such as intrusion detection, system health monitoring, and fraud detection in credit card transactions. In this paper, we describe a new method for detecting anomalous behavior over network perform...

Anomaly detection with Machine learning

A hybrid machine learning approach to network anomaly detection

Zero-day cyber attacks such as worms and spy-ware are becoming increasingly widespread and dangerous. The existing signature-based intrusion detection mechanisms are often not sufficient in detecting these types of attacks. As a result, anomaly intrusion detection methods have been developed to cope with such attacks. Among the variety of anomaly detection approaches, the Support Vector Machine...

Behavior Analysis Using Unsupervised Anomaly Detection

The detection of anomalous behavior in log and sensor data is an often requested task for many data mining applications. If there are no labels available in the dataset as in many real-world setups, unsupervised anomaly detection would be the method of choice. Since these algorithms are not directly applicable on the data in general, an appropriate transformation has to be performed first. This...