Detection of Anomalous Variations in Dynamic Networks

Summary

The project aims to develop better algorithms for the modelling and study of dynamic networks.

Supervisor(s)

Professor Albert Y. Zomaya

Research Location

Computer Science

Program Type

Masters/PHD

Synopsis

Over recent years data networks have experienced significant growth in size and complexity resulting in an increase in frequency, type and severity of network problems. To ensure early detection and identification of these problems better network management techniques must be employed. In the management of large enterprise intranets (data networks), it becomes difficult to detect and identify causes of abnormal change in traffic distributions when the underlying logical topology is dynamic. Network management techniques use statistical trending methods and visualization tools to monitor network performance. These techniques are good for managing traffic but can be inadequate when networks are very dynamic (physical and logical structures of time-varying nature added to traffic variations). This project aims to complement these existing techniques with suitable metrics that allow the automatic detection of significant change within a network and alert operators to when and where the change occurred. Applications are manifold: discovery and prediction of network faults and abnormalities, overload, congestion, hotspots, etc. Possible topics: network reconstruction out of routing tables, where to put (a given number of) probes in order to get maximal coverage of network abnormalities, how does network monitoring depend on network protocols? If one has a time series of network transaction files, can one not monitor network (when?) and not loose too much information? What to do if there are “holes” in time series or in network(s)? In other words: Can a network be monitored without full knowledge of the entire network (network inference?)

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Keywords

telecommunication networks, networking, modelling and simulation, protocols, optimization, ICT

Opportunity ID

The opportunity ID for this research opportunity is: 983

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