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Machine Learning White-Hat Worm Launcher for Tactical Response by Zoning in Botnet Defense System.

Xiangnan Pan¹, Shingo Yamaguchi¹

¹Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube 755-8611, Japan.

Sensors (Basel, Switzerland)

|July 9, 2022

Summary

This summary is machine-generated.

This study introduces a machine learning-based system to combat malicious botnets in IoT networks. The novel approach strategically deploys defensive worms, reducing infected devices by 30% for enhanced cybersecurity.

Keywords:

BDS IoT Petri net botnet machine learning (ML)white-hat zoning

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Area of Science:

Cybersecurity
Machine Learning
Internet of Things (IoT)

Background:

Malicious botnets, like Mirai, pose significant threats to IoT network security.
Existing Botnet Defense Systems (BDS) lack strategic placement of defensive (white-hat) worms.

Purpose of the Study:

To propose a novel machine learning (ML)-based white-hat Worm Launcher for tactical response within the BDS.
To enhance the BDS by introducing zoning for strategic deployment of white-hat worms.

Main Methods:

Introduction of the zoning concept to analyze biased spread of malicious botnets in IoT networks.
Development of an ML-based Worm Launcher to divide networks into zones and implement tactical responses.
Modeling the BDS with the proposed Launcher using agent-oriented Petri nets for evaluation.

Main Results:

The proposed ML-based Worm Launcher enables tactical responses tailored to specific network zones.
Evaluation demonstrates a reduction in infected IoT devices by approximately 30%.

Conclusions:

The novel ML-based Worm Launcher significantly improves the effectiveness of the Botnet Defense System.
Zoning and tactical response strategies are crucial for mitigating botnet threats in IoT environments.