cic-ids2018
CIC-IDS2018LICENSE
You may redistribute, republish, and mirror the CSE-CIC-IDS2018 dataset in any form. However, any use or redistribution of the data must include a citation to the CSE-CIC-IDS2018 dataset and a link to this page in AWS.
Research paper outlining the details of analyzing the similar IDS/IPS dataset and related principles:
Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization”, 4th… See the full description on the dataset page: https://huggingface.co/datasets/c01dsnap/CIC-IDS2018.CAD-CICIDS2018
CAD-CICIDS2018
Dataset Summary
CAD-CICIDS2018 is a single-source continual anomaly detection benchmark scenario for network intrusion detection. It is derived from CSE-CIC-IDS2018 and converts the original tabular network-intrusion data into a sequence of concept-grouped tasks.
The dataset contains 2,590,771 samples, 5 tasks, and has a reported 28.04% anomaly ratio in the test set.
Intended Use
This dataset is intended for research on:
continual… See the full description on the dataset page: https://huggingface.co/datasets/lifelonglab/CAD-CICIDS2018.CICIDS2018cicids2018CIC-IDS-2018
CSE-CIC-IDS 2018 Network Intrusion Detection Dataset
Description
The CSE-CIC-IDS2018 dataset was developed by the Communications Security
Establishment (CSE) and the Canadian Institute for Cybersecurity (CIC).
It includes seven different attack scenarios: Brute-force, Heartbleed,
Botnet, DoS, DDoS, Web attacks, and infiltration of the network from inside.
Network traffic was captured using CICFlowMeter and processed into
bidirectional flow features.… See the full description on the dataset page: https://huggingface.co/datasets/AhmedMahmoud165/CIC-IDS-2018.CSE-CIC-IDS2018-V2This is the updated version CSE-CIC-IDS 2018 dataset. The data is normalised and 1 new class "Comb" which is a combination of existing attacks is added.
To cite the dataset, please reference the original paper with DOI: 10.1109/SmartNets61466.2024.10577645. The paper is published in IEEE SmartNets and can be accessed here: https://www.researchgate.net/publication/382034618_Blender-GAN_Multi-Target_Conditional_Generative_Adversarial_Network_for_Novel_Class_Synthetic_Data_Generation.
Citation… See the full description on the dataset page: https://huggingface.co/datasets/abluva/CSE-CIC-IDS2018-V2.