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.

翻译：物联网的兴起为计算机网络带来了额外的复杂性和挑战。由于这些设备设计简单，它们特别容易受到网络攻击。因此，在网络中识别这些设备对于网络管理和检测恶意行为至关重要。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，主流方法严重依赖机器学习。然而，机器学习方法需要选择特征、调整超参数并重新训练模型，才能获得最佳结果并对网络中检测到的概念漂移具有鲁棒性。在本研究中，我们建议使用局部敏感哈希来进行网络流量指纹识别，以解决这些难题。我们的研究重点考察了Nilsimsa LSH函数的多种设计方案。然后，我们利用该函数为网络数据创建独特的指纹，这些指纹可用于识别设备。我们还将该方法与基于机器学习的流量指纹识别进行了比较，观察到我们的方法将当前最优技术的准确率提高了12%，在网络中识别设备的准确率达到了约94%。