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Related Experiment Video

Updated: Oct 20, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Published on: March 2, 2015

Dataset for Botnet group activity with adaptive generator.

Dandy Pramana Hostiadi^1,2, Tohari Ahmad¹

¹Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.

|September 10, 2021

Summary

This summary is machine-generated.

This dataset simulates botnet activity, offering 13 scenarios for network traffic analysis. It aids researchers in detecting periodic bot group activity and its intensity using net flow features.

Keywords:

Bot correlation activity Bot group activities Botnet dataset Infrastructure Network security

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Area of Science:

Cybersecurity
Network Security
Data Science

Background:

Botnet activity poses a significant threat to network security.
Existing datasets may not fully capture the dynamic nature of botnet group activities.
Simulated datasets are crucial for developing and testing detection methods.

Purpose of the Study:

To present a novel dataset simulating bot group activities.
To provide a resource for researching periodic botnet behavior and intensity.
To facilitate the development of advanced botnet detection techniques.

Main Methods:

Dataset generation based on the CTU dataset simulation process.
Modeling approach to create botnet group activity scenarios.
Extraction of activity patterns and net flow features.
Inclusion of 13 distinct datasets with varying bot numbers and activity types.

Main Results:

A comprehensive dataset comprising 13 simulation scenarios.
Each dataset contains 8 hours of network flow data.
Features include 14 net flow attributes and one activity label (bot or normal).
The dataset captures periodic bot group activity patterns and intensity.

Conclusions:

The presented dataset is valuable for cybersecurity research.
It enables studies on the detection and analysis of botnet activities.
Useful for understanding the correlation between bot activity types and network traffic.