Public domain datasets for optimizing network intrusion and machine learning approaches

Maznan Deraman, Abd Jalil Desa, Zulaiha Ali Othman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Network intrusion detection system (NIDS) commonly attributed to the task to mitigate network and security attacks that has potential to compromise the safety of a network resources and its information. Research in this area mainly focuses to improve the detection method in network traffic flow. Machine learning techniques had been widely used to analyze large datasets including network traffic. In order to develop a sound mechanism for NIDS detection tool, benchmark datasets is required to assist the data mining process. This paper presents the benchmark datasets available publicly for NIDS study such as KDDCup99, IES, pcapr and others. We use some popular machine learning tools to visualize the properties and characteristics of the benchmark datasets.

Original languageEnglish
Title of host publicationConference on Data Mining and Optimization
Pages51-56
Number of pages6
DOIs
Publication statusPublished - 2011
Event2011 3rd Conference on Data Mining and Optimization, DMO 2011 - Putrajaya
Duration: 28 Jun 201129 Jun 2011

Other

Other2011 3rd Conference on Data Mining and Optimization, DMO 2011
CityPutrajaya
Period28/6/1129/6/11

Fingerprint

Intrusion detection
Learning systems
Data mining
Acoustic waves

Keywords

  • Benchmark Dataset Repository
  • Machine Learning
  • Network Intrusion Detection

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

Cite this

Deraman, M., Jalil Desa, A., & Ali Othman, Z. (2011). Public domain datasets for optimizing network intrusion and machine learning approaches. In Conference on Data Mining and Optimization (pp. 51-56). [5976504] https://doi.org/10.1109/DMO.2011.5976504

Public domain datasets for optimizing network intrusion and machine learning approaches. / Deraman, Maznan; Jalil Desa, Abd; Ali Othman, Zulaiha.

Conference on Data Mining and Optimization. 2011. p. 51-56 5976504.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Deraman, M, Jalil Desa, A & Ali Othman, Z 2011, Public domain datasets for optimizing network intrusion and machine learning approaches. in Conference on Data Mining and Optimization., 5976504, pp. 51-56, 2011 3rd Conference on Data Mining and Optimization, DMO 2011, Putrajaya, 28/6/11. https://doi.org/10.1109/DMO.2011.5976504
Deraman M, Jalil Desa A, Ali Othman Z. Public domain datasets for optimizing network intrusion and machine learning approaches. In Conference on Data Mining and Optimization. 2011. p. 51-56. 5976504 https://doi.org/10.1109/DMO.2011.5976504
Deraman, Maznan ; Jalil Desa, Abd ; Ali Othman, Zulaiha. / Public domain datasets for optimizing network intrusion and machine learning approaches. Conference on Data Mining and Optimization. 2011. pp. 51-56
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