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1. Detection of Anomalous Variations in Dynamic Networks The intranet is fast becoming the
preferred enterprise solution for delivering interoperable communications for
internal information exchange. The term intranet implies a private data
network that makes use of communication protocols and services of the
Internet, such as the TCP/IP protocol suite. Over recent years these 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?) 2. New
Methods for Modelling Dynamic Communication Networks This project investigates possible applications
of: Dynamical systems (discrete, continuous, and hybrid) in modelling the of
dynamic communications/information networks, networks of dynamical systems
(users are modelled as dynamical systems) in behaviour modelling of time
series of communications/information networks, complex systems in modelling
of large and dynamic communications/information networks, Computational and
statistical mechanics in modelling and behavioural analysis of
communications/information networks. 3. The Choice of Appropriate Difference Measures A number of network similarity
(dissimilarity/distance) measures have been developed in the Intelligent
Networks Group of DSTO. This project investigates the relationship between
those measures and particular networks (protocols, routing algorithms, etc.)
Possible topics of interest: Which measures detect best abnormal/significant
changes in which networks (classified with respect to protocols, size,
traffic variations, user population, etc.), what to do with multi-layered
situations (physical/logical/social networks), comparisons of networks,
localization of change - discover the areas/users of network that contribute
most to change/abnormalities in network behaviour, what to do with massive
networks? Which measures are more appropriate? The need to worry about
computing times for certain measures? Should the algorithms be tweaked
(parallelized) to speed up the processes?
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