僵尸网络防御系统中基于机器学习的白帽蠕虫启动器

Xiangnan Pan, S. Yamaguchi, Takumi Kageyama, Mohd Hafizuddin Bin Kamilin
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引用次数: 9

摘要

针对僵尸网络防御系统(BDS),提出了一种基于机器学习(ML)的适用于大规模物联网网络的白帽蠕虫启动器。北斗系统是一种利用白帽蠕虫来消灭恶意僵尸网络的网络安全系统。白帽蠕虫保护物联网系统免受恶意机器人的攻击,BDS决定白帽蠕虫的数量,但没有讨论白帽蠕虫在物联网网络中的部署。因此,作者提出了一种基于机器学习的启动器来有效地启动白帽蠕虫,并采用分而治之的算法将启动器部署到大规模物联网网络中。然后利用面向agent的Petri网对BDS和发射器进行建模,并通过PN2模型的仿真验证了效果。结果表明,提出的启动程序可以将受感染设备的数量减少约30-40%。
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Machine-Learning-Based White-Hat Worm Launcher in Botnet Defense System
This article proposes a white-hat worm launcher based on machine learning (ML) adaptable to large-scale IoT network for Botnet Defense System (BDS). BDS is a cyber-security system that uses white-hat worms to exterminate malicious botnets. White-hat worms defend an IoT system against malicious bots, the BDS decides the number of white-hat worms, but there is no discussion on the white-hat worms' deployment in IoT network. Therefore, the authors propose a machine-learning-based launcher to launch the white-hat worms effectively along with a divide and conquer algorithm to deploy the launcher to large-scale IoT networks. Then the authors modeled BDS and the launcher with agent-oriented Petri net and confirmed the effect through the simulation of the PN2 model. The result showed that the proposed launcher can reduce the number of infected devices by about 30-40%.
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