Experimental Documentation

Citation Author(s):
hao
fu
Submitted by:
hao fu
Last updated:
Wed, 04/02/2025 - 03:31
DOI:
10.21227/f1qz-h190
Research Article Link:
A Relation-based Method for Robust Malicious Traffic Detection
License:
Creative Commons Attribution
7 Views
Categories:
Security
Keywords:
Encrypted Traffic; Features; Configuration
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Abstract 

This dataset is specifically designed to support research related to TIFS(IEEE Transactions on Information Forensics and Security)papers.It offers a comprehensive collection of raw data,extracted feature data,and detailed parameter profiles for the models used in the studies.The raw data includes a wide range of measurements and observations,providing a solid foundation for further analysis.The extracted feature data highlights key characteristics and patterns,making it easier for researchers to identify important trends and insights.Additionally,the detailed parameter profiles offer in-depth information on the configuration and performance of the models,allowing researchers to reproduce and build upon the results.This dataset is a valuable resource for anyone working in the field of information forensics and security,aiming to facilitate high-quality research and innovation.

Instructions: 

config.yml is the model configuration file, Patator is the attack dataset, FlowNetoutFeaPatator.json is the extracted features, and all files are combined with the code: https://github.com/gswsfh/BSTS-Net.git

Comments

Very helpful dataset

Submitted by Swetha Vankayalapati on Wed, 04/02/2025 - 04:28

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