用于网络流量指纹识别的局部敏感哈希
Locality Sensitive Hashing for Network Traffic Fingerprinting
AI总结：
针对物联网设备易受攻击且传统机器学习流量指纹识别方法存在需特征选择、超参数调整及模型重训练等局限，本文提出使用局部敏感哈希（LSH）特别是 Nilsimsa 函数生成网络数据指纹以识别设备，准确率达约 94%，较现有最优方法提升 12%。
摘要：
物联网（IoT）的出现给计算机网络带来了额外的复杂性和困难。这些设备由于其简约设计而特别容易受到网络攻击。因此，为了网络管理以及识别任何有害行为，在网络中识别这些设备至关重要。网络流量指纹识别是识别设备和检测异常的关键技术。目前，用于此目的的主流方法严重依赖机器学习（ML）。然而，机器学习（ML）方法需要特征选择、超参数调整和模型重训练，以获得最佳结果并对网络中检测到的概念漂移提供弹性。在这项研究中，我们建议使用局部敏感哈希（LSH）进行网络流量指纹识别，以解决这些困难。我们的研究重点是检查 Nilsimsa LSH 函数的几种设计选项。然后我们使用此函数为网络数据创建独特的指纹，可用于识别设备。我们还将它与基于 ML 的流量指纹识别进行了比较，观察到我们的方法将最先进技术的准确率提高了 12%，在网络中识别设备时达到约 94% 的准确率。
英文摘要：
The advent of the Internet of Things (IoT) has brought forth additional intricacies and difficulties to computer networks. These gadgets are particularly susceptible to cyber-attacks because of their simplistic design. Therefore, it is crucial to recognise these devices inside a network for the purpose of network administration and to identify any harmful actions. Network traffic fingerprinting is a crucial technique for identifying devices and detecting anomalies. Currently, the predominant methods for this depend heavily on machine learning (ML). Nevertheless, machine learning (ML) methods need the selection of features, adjustment of hyperparameters, and retraining of models to attain optimal outcomes and provide resilience to concept drifts detected in a network. In this research, we suggest using locality-sensitive hashing (LSH) for network traffic fingerprinting as a solution to these difficulties. Our study focuses on examining several design options for the Nilsimsa LSH function. We then use this function to create unique fingerprints for network data, which may be used to identify devices. We also compared it with ML-based traffic fingerprinting and observed that our method increases the accuracy of state-of-the-art by 12% achieving around 94% accuracy in identifying devices in a network.
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。
