In recent years, Internet of Things (IoT) security has attracted significant interest by researchers due to new characteristics of IoT such as heterogeneity of devices, resource constraints, and new types of attacks targeting IoT. Intrusion detection, which is an indispensable part of a security system, is also included in these studies. In order to explore the complex characteristics of IoT, machine learning methods, which rely on long training time to generate intrusion detection models, are proposed in the literature. Furthermore, these systems need to learn a new/fresh model from scratch when the environment changes. This study explores the use of transfer learning in order to generate intrusion detection algorithms for such dynamically changing IoT. Transfer learning is an approach that stores knowledge learned from a problem domain/task and applies that knowledge to another problem domain/task. Here, it is employed in the following two settings: transferring knowledge for generating suitable intrusion algorithms for new devices, transferring knowledge for detecting new types of attacks. In this study, Routing Protocol for Low-Power and Lossy Network (RPL), a routing protocol for resource-constrained wireless networks, is used as an exemplar protocol and specific attacks against RPL are targeted. The experimental results show that the transfer learning approach gives better performance than the traditional approach. Moreover, the proposed approach significantly reduces learning time, which is an important factor for putting devices/networks in operation in a timely manner. Even though transfer learning has been considered a potential candidate for improving IoT security, to the best of our knowledge, this is the first application of transfer learning under these two settings in RPL-based IoT networks.

中文翻译：

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用于保护资源受限的物联网设备的迁移学习方法


近年来，由于物联网的新特征，如设备异构性、资源限制以及针对物联网的新型攻击等，物联网安全引起了研究人员的极大兴趣。入侵检测作为安全系统不可或缺的一部分，也包含在这些研究中。为了探索物联网的复杂特征，文献中提出了依赖长时间训练来生成入侵检测模型的机器学习方法。此外，当环境发生变化时，这些系统需要从头开始学习新的/新鲜的模型。本研究探讨了如何使用迁移学习来为这种动态变化的物联网生成入侵检测算法。迁移学习是一种存储从问题域/任务中学到的知识并将该知识应用于另一个问题域/任务的方法。这里，它用于以下两种设置：传输知识以生成适合新设备的入侵算法，传输知识以检测新类型的攻击。在本研究中，低功耗有损网络路由协议（RPL）是一种用于资源受限无线网络的路由协议，被用作示例协议，并针对针对 RPL 的特定攻击。实验结果表明，迁移学习方法比传统方法具有更好的性能。此外，所提出的方法显着减少了学习时间，这是设备/网络及时投入运行的重要因素。 尽管迁移学习被认为是提高物联网安全性的潜在候选者，但据我们所知，这是迁移学习在基于 RPL 的物联网网络中这两种设置下的首次应用。