论文标题
灯泡中的网络入侵检测系统
Network Intrusion Detection System in a Light Bulb
论文作者
论文摘要
物联网（IoT）设备正在逐渐被用于各种边缘应用程序来监视和控制家庭和行业基础架构。由于计算和能源资源有限，在许多物联网设备中，主动安全保护通常是最小的。这引起了关键的安全挑战，吸引了研究人员在网络安全领域的关注。尽管有大量提出的网络入侵检测系统（NIDS），但对实践实施的研究有限，据我们所知，尚未证明没有基于边缘的NID可以在大多数IOT设备中发现的常见低功率芯片组中运行，例如ESP8266。这项研究旨在通过突破基于低功率机器学习（ML）NIDSS的界限来解决这一差距。我们建议并开发高效且低功率的基于ML的NID，并通过在典型的智能灯泡上运行它对物联网应用程序的适用性。我们还针对其他提出的基于边缘的NIDS评估了我们的系统，并表明我们的模型具有更高的检测性能，并且更快，更小，因此更适用于更广泛的物联网边缘设备。
Internet of Things (IoT) devices are progressively being utilised in a variety of edge applications to monitor and control home and industry infrastructure. Due to the limited compute and energy resources, active security protections are usually minimal in many IoT devices. This has created a critical security challenge that has attracted researchers' attention in the field of network security. Despite a large number of proposed Network Intrusion Detection Systems (NIDSs), there is limited research into practical IoT implementations, and to the best of our knowledge, no edge-based NIDS has been demonstrated to operate on common low-power chipsets found in the majority of IoT devices, such as the ESP8266. This research aims to address this gap by pushing the boundaries on low-power Machine Learning (ML) based NIDSs. We propose and develop an efficient and low-power ML-based NIDS, and demonstrate its applicability for IoT edge applications by running it on a typical smart light bulb. We also evaluate our system against other proposed edge-based NIDSs and show that our model has a higher detection performance, and is significantly faster and smaller, and therefore more applicable to a wider range of IoT edge devices.
