Data

CIC-BoT-IoT

The University of Queensland
Associate Professor Marius Portmann (Aggregated by) Associate Professor Marius Portmann (Aggregated by) Dr Siamak Layeghy (Aggregated by) Dr Siamak Layeghy (Aggregated by) Mr Mohanad Sarhan (Aggregated by)
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.48610/c80fccd&rft.title=CIC-BoT-IoT&rft.identifier=RDM ID: f80dbd20-ef9c-11ed-8007-832d3bc76ba8&rft.publisher=The University of Queensland&rft.description=The CICFlowMeter format of the datasets is made up of 83 network features. The details of the datasets are published in: Mohanad Sarhan, Siamak Layeghy, and Marius Portmann, Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection, Big Data Research, 30, 100359, 2022 The use of the datasets for academic research purposes is granted in perpetuity after citing the above papers. For commercial purposes, it should be agreed upon by the authors. Please get in touch with the author Mohanad Sarhan for more details.&rft.creator=Associate Professor Marius Portmann&rft.creator=Associate Professor Marius Portmann&rft.creator=Dr Siamak Layeghy&rft.creator=Dr Siamak Layeghy&rft.creator=Mr Mohanad Sarhan&rft.creator=Mr Mohanad Sarhan&rft.date=2023&rft_rights=2023, The University of Queensland&rft_rights= http://guides.library.uq.edu.au/deposit_your_data/terms_and_conditions&rft_subject=eng&rft_subject=Computer System Security&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft_subject=COMPUTER SOFTWARE&rft.type=dataset&rft.language=English Access the data
Cite Saved to MyRDA Save to MyRDA

Licence & Rights:

Other view details

Access:

Open

Contact Information

s4608505@student.uq.edu.au
School of Information Technology and Electrical Engineering

Full description

The CICFlowMeter format of the datasets is made up of 83 network features. The details of the datasets are published in: Mohanad Sarhan, Siamak Layeghy, and Marius Portmann, Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection, Big Data Research, 30, 100359, 2022 The use of the datasets for academic research purposes is granted in perpetuity after citing the above papers. For commercial purposes, it should be agreed upon by the authors. Please get in touch with the author Mohanad Sarhan for more details.

Issued: 15 05 2023

This dataset is part of a larger collection

Click to explore relationships graph
Help

Related People

Related Grants and Projects

Related Websites

Subjects
Computer Software | Computer System Security | Information and Computing Sciences | eng |

User Contributed Tags    

Login to tag this record with meaningful keywords to make it easier to discover

Other Information
Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-Based Network Intrusion Detection

local : UQ:04e8543

Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2022). Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-Based Network Intrusion Detection. Big Data Research, 30 100359, 1-9. doi: 10.1016/j.bdr.2022.100359

Research Data Collections

local : UQ:289097

Identifiers
Similar data you may be interested in: