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Friday, June 6, 2025
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  • NIMS Lab Students took the top place at the in the Atlantic Region CyberSci Challenge 2022/2023. What’s more, they ranked the fourth nationally. Way to go Team! 
  • AI Areas & Challenges w/ Dr. Nur Zincir-Heywood
  • AI and Evolutionary Computation Experts Q&A | Malcolm Heywood | Cognizant
  • Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning
  • IEEE/IFIP Best Poster Award
Network Information Management and Security Group
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Publications

 

Dr. Nur Zincir-Heywood


Dr. Malcolm Heywood

GP Bibliography

 

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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