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Botnet dataset with simultaneous attack activity.

Muhammad Aidiel Rachman Putra¹, Dandy Pramana Hostiadi², Tohari Ahmad¹

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

|November 25, 2022

Summary

This summary is machine-generated.

This study introduces a new dataset for detecting simultaneous botnet attacks, crucial for cybersecurity. The dataset aids in developing efficient parallel computation detection methods for botnet network traffic.

Keywords:

Bot communication behavior Bot group activities Botnet dataset Infrastructure Network security

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Area of Science:

Cybersecurity and Network Forensics
Data Science and Machine Learning for Network Security

Background:

Botnet attacks pose a significant threat to network security, often involving complex and simultaneous activities.
Existing datasets may not fully capture the characteristics of concurrent botnet operations, hindering detection model development.
Bidirectional network flow (binetflow) is a key representation of network traffic patterns.

Purpose of the Study:

To propose and generate a novel dataset exhibiting simultaneous botnet attack activities.
To provide a resource for training and evaluating detection systems for concurrent botnet threats.
To facilitate research into efficient parallel computation detection methods for botnet spread.

Main Methods:

Simulation-based dataset generation using botnet behavior patterns extracted from CTU-13 and NCC datasets.
Incorporation of normal network activities alongside simulated botnet traffic.
Dataset structured with 18 network header features across three detection sensors, totaling 8 hours of recordings.

Main Results:

A comprehensive dataset capturing the dynamics of simultaneous botnet attacks.
The dataset includes diverse botnet types, reflecting realistic attack scenarios.
The generated data supports the analysis of botnet spread and the testing of detection algorithms.

Conclusions:

The proposed dataset is valuable for advancing the field of botnet detection, particularly for simultaneous attacks.
Efficient processing, such as parallel computation detection, is essential for handling the scale of modern botnet threats.
This resource will aid in developing more robust and effective cybersecurity defenses against evolving botnet tactics.