The production of a large-scale monitoring system for a high-speed network leads to a number of challenges. These challenges are not purely techinical but also socio-political and legal. The number of stakeholders in a such a monitoring activity is large including the network operators, the users, the equipment manufacturers and of course the monitoring researchers. The MASTS project (Measurement at All Scales in Time and Space) was created to instrument the high-speed JANET Lightpath network, and has been extended to incorporate other paths supported by JANET(UK).Challenges the project has faced have included: simple access to the network; legal issues involved in the storage and dissemination of the captured information, which may be personal; the volume of data captured and the rate at which this data appears at store. To this end the MASTS system will have established four monitoring points each capturing packets on a high speed link. Traffic header data will be continuously collected, anonymised, indexed, stored and made available to the research community. A legal framework for the capture and storage of network measurement data has been developed which allows the anonymised IP traces to be used for research purposes.
Abstract:The scheduling of railway trains has been a research problem for many years. Many of the choices required are not known a priori and require exploration of the problem to determine them. A modular Genetic system was designedmake the evaluation function and preparation of the timetable tractable. The Genetic system consists of a Genome, split into Chromosomes so the extra choices that become known throughout the evolution can be added to the Chromosomes. A weighted fitness function and a multiobjective non-dominated fitness function were tried, and then partial objective ranking was added. The system has tackled a mixture of problems has produced promising results.
Abstract-Honeypots are a useful tool for discovering the distribution of malicious traffic on the Internet and how that traffic evolves over time. In addition, they allow an insight into new attacks appearing. One major problem is analysing the large amounts of data generated by such honeypots and correlating between multiple honeypots. Honey Plotter is a web-based query and visualisation tool to allow investigation into data gathered by a distributed honeypot network. It is built on top of a relational database, which allows great flexibility in the questions that can be asked and has automatic generation of visualisations based on the results of queries. The main focus is on aggregate statistics but individual attacks can also be analysed. Statistical comparison of distributions is also provided to assist with detecting anomalies in the data; helping separate out common malicious traffic from new threats and trends. Two short case studies are presented to give an example of the types of analysis that can be performed.
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