A Framework For Comparing Different Machine Learning Algorithm Models For Intrusion Detection In loT Environment

S. Unnikrishnan, S. Gokul Krishna, S. Krishna
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Abstract

loT-based systems can be seen in different areas like healthcare, transportation, farming, the power infrastructure, and manufacturing. Even though the Internet of Things can make people's lives easier, its exponential growth makes it a popular target for cyber-criminals and is subject to significant threats. One of the most devastating attacks is the denial of service (DoS), which prevents legitimate users from accessing services they have paid for. Therefore, There is an urgent requirement of loT-specific intrusion detection systems to tackle all these cyber-attacks. Numerous lightweight protocols are there to secure the communication between the loT devices. Here we used the IDS data set of the most critical loT communication protocol known as Message Queuing Telemetry Transport (MQTT) The information will be used as the foundation for developing creative intrusion detection method in loT networks. This work focused on developing a framework to compare several machine learning algorithms, and display the performance result of each one. The result demonstrated the most accurate model and the importance of using the machine learning-based IDS.
一种用于loT环境下入侵检测的不同机器学习算法模型比较框架
基于lot的系统可以在医疗保健、交通、农业、电力基础设施和制造业等不同领域看到。尽管物联网可以使人们的生活更轻松,但其指数级增长使其成为网络犯罪分子的热门目标,并受到重大威胁。最具破坏性的攻击之一是拒绝服务(DoS),它阻止合法用户访问他们已付费的服务。因此,迫切需要针对批次的入侵检测系统来应对这些网络攻击。有许多轻量级协议用于保护loT设备之间的通信。在这里,我们使用了最关键的loT通信协议——消息队列遥测传输(MQTT)的IDS数据集,这些信息将作为开发创新型loT网络入侵检测方法的基础。这项工作的重点是开发一个框架来比较几种机器学习算法,并显示每种算法的性能结果。结果证明了最准确的模型和使用基于机器学习的IDS的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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