The proliferation of the Internet of Things (IoT) has introduced a massive influx of devices into the market, bringing with them significant security vulnerabilities. In this diverse ecosystem, robust IoT device identification is a critical preventive measure for network security and vulnerability management. This study proposes a deep learning-based method to identify IoT devices using the Aalto dataset. We employ Convolutional Neural Networks (CNN) to classify devices by converting network packet payloads into pseudo-images. Furthermore, we compare the performance of this payload-based approach against a feature-based fingerprinting method. Our results indicate that while the fingerprint-based method is significantly faster (approximately 10x), the payload-based image classification achieves comparable accuracy, highlighting the trade-offs between computational efficiency and data granularity in IoT security.

翻译：物联网（IoT）的快速发展导致大量设备涌入市场，同时带来了严重的安全隐患。在这一多样化的生态系统中，稳健的物联网设备识别是网络安全和漏洞管理的关键预防措施。本研究提出一种基于深度学习的方法，利用Aalto数据集识别物联网设备。我们采用卷积神经网络（CNN），将网络数据包载荷转换为伪图像以对设备进行分类。此外，我们将这种基于载荷的方法与基于特征的指纹识别方法进行了性能比较。结果表明，虽然基于指纹的方法速度显著更快（约10倍），但基于载荷的图像分类能达到相当的准确率，这凸显了物联网安全中计算效率与数据粒度之间的权衡。