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  • IEEE/IFIP Best Poster Award
  • ACM Best Paper Award 2018!
  • Humies Award 2018!
  • Best Paper Nomination 2018
  • NSERC Undergrad Research Awards 2018
Network Information Management and Security Group

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Dalhousie University

Network Information Management and Security Group

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Award 

NSERC Undergrad Research Awards 2018

February 14, 2019February 15, 2019 Nur Zincir-Heywood

Congratulations to NIMS Lab members Ryan and Samuel for receiving the very prestigious NSERC Undergraduate Summer Research Awards in 2018!

  • ← EuroGP’2017 Best Paper Award!
  • Best Paper Nomination 2018 →

Resources

NIMS Data Sets
NetMate is employed to generate flows and compute feature values on the above data sets. Data sets are available for researchers in ARFF/CSV format which are ready to be used with Weka.

SBB
SBB is a form of genetic programming based learning algorithm which is designed and developed to solve tasks using a co-evolutionary approach.

Tcpreplay
Tcpreplay is a suite of BSD GPLv3 licensed tools written by Aaron Turner for UNIX (and Win32 under Cygwin) operating systems which gives you the ability to use previously captured traffic to test a variety of network devices.

NetMate
We use NetMate at NIMS to convert capture files of network traffic into flow statistics. NetMate lets you generate a comma separated value file containing flows from a capture file.

Tranalyzer
Tranalyzer2 is a lightweight flow generator and packet analyzer application designed for researchers with an emphasis on simplicity, performance, and scalability.

Softflowd
Softflowd is flow-based network traffic analyser capable of Cisco NetFlow data export. Softflowd semi-statefully tracks traffic flows recorded by listening on a network interface or by reading a packet capture file.

Circos
Circos is a software package for visualizing data and information. It visualizes data in a circular layout – this makes Circos ideal for exploring relationships between objects or positions.

Weka
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code.

MOA
MOA is an open source framework for data stream mining. It includes a collection of machine learning algorithms including classification, regression, clustering, outlier detection, concept drift detection, and recommender systems.

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