The Industrial Revolution 4.0 began with the breakthrough technological advances in 5G, and artificial intelligence has innovatively transformed the manufacturing industry from digitalization and automation to the new era of smart factories. A smart factory can do not only more than just produce products in a digital and automatic system, but also is able to optimize the production on its own by integrating production with process management, service distribution, and customized product requirement. A big challenge to the smart factory is to ensure that its network security can counteract with any cyber attacks such as botnet and Distributed Denial of Service, They are recognized to cause serious interruption in production, and consequently economic losses for company producers. Among many security solutions, botnet detection using honeypot has shown to be effective in some investigation studies. It is a method of detecting botnet attackers by intentionally creating a resource within the network with the purpose of closely monitoring and acquiring botnet attacking behaviors. For the first time, a proposed model of botnet detection was experimented by combing honeypot with machine learning to classify botnet attacks. A mimicking smart factory environment was created on IoT device hardware configuration. Experimental results showed that the model performance gave a high accuracy of above 96%, with very fast time taken of just 0.1 ms and false positive rate at 0.24127 using random forest algorithm with Weka machine learning program. Hence, the honeypot combined machine learning model in this study was proved to be highly feasible to apply in the security network of smart factory to detect botnet attacks.

中文翻译：

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结合使用蜜罐和机器学习的IoT智能工厂中的僵尸网络攻击分类

工业革命4.0始于5G的突破性技术进步，而人工智能已将制造业从数字化和自动化创新地转变为智能工厂的新时代。智能工厂不仅可以在数字和自动化系统中生产产品，而且还可以通过将生产与流程管理，服务分配和定制产品需求相集成来自行优化生产。智能工厂的一大挑战是确保其网络安全性可以抵御僵尸网络和分布式拒绝服务之类的任何网络攻击，它们被认为会严重中断生产，从而给公司生产商造成经济损失。在许多安全解决方案中，在某些调查研究中，使用蜜罐进行的僵尸网络检测已证明是有效的。它是一种通过密切监视和获取僵尸网络攻击行为而有意在网络内创建资源来检测僵尸网络攻击者的方法。第一次，通过结合蜜罐和机器学习对僵尸网络攻击进行分类，对提出的僵尸网络检测模型进行了实验。在物联网设备硬件配置上创建了一个模仿智能工厂的环境。实验结果表明，使用带有Weka机器学习程序的随机森林算法，该模型的性能具有96％以上的高精度，仅需0.1 ms的非常快的时间以及0.24127的假阳性率。因此，