Optimized testing of modern protection equipment using transient simulation

Mempel, C.; Cialla, B.; Luxenburger, R.

doi:10.1049/cp.2010.0340

Cited by 3 publications

(1 citation statement)

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“…They describe the basis of simulation tools in this environment, which must be intuitive, simple to use and flexible. Mempel et al (2010), the authors propose the usage of transient simulation rather than traditional methods on testing modern protection equipments, presenting the advantages of this change through the analysis of different applications. Canova et al (1999), the authors use simulation processes on the development of a weapon called Predator SRAW, or short range attack weapon.…”

Section: Related Workmentioning

confidence: 99%

Scorpius: sFlow Network Anomaly Simulator

Assis¹,

Proença²

2015

Journal of Computer Science

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Due to the increasing amount of data traveling computer networks every day, efficient management of this information is required to ensure the quality of the services provided by them. Development of new network management tools and mechanisms is a widely approached area due to its importance, not only to the current technology, but also to next generation network standards and equipments. Several researches have been directed to the use of IP Flows in order to increase the efficiency of these management tools. Although there are several proposed approaches in this area, most of them don't have suitable test scenarios to validate their performance results. In this study, we present Scorpius, a new simulation tool able to help testing network management mechanisms based on IP Flows. Scorpius is capable of simulating different kinds of anomalies, such as Denial of Service (DoS), Distributed Denial of Service (DDoS), Flash Crowd and Port Scan, directly into the flow export files. This characteristic unites the advantages of tests in real network environments without the drawbacks of the occurrence of real anomalies and attacks, even controlled ones. This approach makes the processes of performance analysis of anomaly detection approaches easier, without interfering or hampering the operation of the analyzed network. In order to validate the efficiency of the presented tool, we use real data collected from a large-scale network environment.

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Section: Related Workmentioning

confidence: 99%