ZEST：面向未见物联网设备分类的基于注意力的零样本学习
ZEST: Attention-based Zero-Shot Learning for Unseen IoT Device Classification
AI总结：
本文提出基于自注意力的零样本学习框架ZEST，通过SANE特征提取、伪数据生成和监督分类，解决训练阶段未见物联网设备的分类问题，并在真实流量上验证其优于基线方法。
摘要：
近期的研究工作提出了用于对连接到网络的物联网设备进行分类的机器学习模型。然而，一个实际挑战仍然存在：在模型训练期间并非所有设备（及其流量）都可用。这本质上意味着，在运行阶段，我们需要对训练阶段未见过的新的设备进行分类。为应对这一挑战，我们提出了 ZEST——一个基于自注意力的零样本学习（ZSL）框架，用于对已见和未见设备进行分类。ZEST 包含：i）一个基于自注意力的网络特征提取器，称为 SANE，用于提取物联网流量的潜在空间表示；ii）一个生成模型，利用潜在特征训练解码器以生成伪数据；iii）一个监督模型，在生成的伪数据上进行训练以对设备进行分类。我们在真实物联网流量数据上进行了大量实验；实验表明：i）ZEST 在准确率方面相比基线方法取得了显著提升；ii）与常用于网络流量建模的 LSTM 相比，SANE 能够更好地提取有意义的表示。
英文摘要：
Recent research works have proposed machine learning models for classifying IoT devices connected to a network. However, there is still a practical challenge of not having all devices (and hence their traffic) available during the training of a model. This essentially means, during the operational phase, we need to classify new devices not seen in the training phase. To address this challenge, we propose ZEST -- a ZSL (zero-shot learning) framework based on self-attention for classifying both seen and unseen devices. ZEST consists of i) a self-attention based network feature extractor, termed SANE, for extracting latent space representations of IoT traffic, ii) a generative model that trains a decoder using latent features to generate pseudo data, and iii) a supervised model that is trained on the generated pseudo data for classifying devices. We carry out extensive experiments on real IoT traffic data; our experiments demonstrate i) ZEST achieves significant improvement (in terms of accuracy) over the baselines; ii) SANE is able to better extract meaningful representations than LSTM which has been commonly used for modeling network traffic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
