CBR -- 通过检索加密网络流量中的分布外样本提升自适应分类
CBR -- Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-distribution
AI总结：
本文提出CBR方法，基于ANN检索实现加密流量分类，无需重训练即可动态识别新类别，性能接近随机森林，可作为现有分类器的补充。
摘要：
加密网络流量分类从不同方法和不同目标来处理该问题。常见的方法之一是在固定数量的类别上使用基于机器学习或深度学习的解决方案，当输入未知类别时会导致错误分类。处理未知类别的解决方案之一是重新训练模型，然而，每次模型过时都重新训练既耗费资源又耗时。因此，越来越需要允许分类模型动态地检测和适应新类别，而无需重新训练，而是能够通过少样本学习[1]检测新类别。在本文中，我们介绍了自适应分类通过检索（CBR），一种用于加密网络流量分类的新方法。我们的新方法基于一种基于ANN的方法，使我们能够有效地识别新类别和现有类别，而无需重新训练模型。这种新方法简单而有效，在分类任务中取得了与RF相似的结果，差异最多为5%（通常更少），而在新样本（来自新类别）的情况下略有下降，无需重新训练。总之，新方法是一种实时分类方法，可以在不重新训练的情况下对新类别进行分类。此外，我们的解决方案可以作为RF或任何其他机器学习/深度学习分类方法的补充解决方案，作为聚合解决方案使用。
英文摘要：
Encrypted network traffic Classification tackles the problem from different approaches and with different goals. One of the common approaches is using Machine learning or Deep Learning-based solutions on a fixed number of classes, leading to misclassification when an unknown class is given as input. One of the solutions for handling unknown classes is to retrain the model, however, retraining models every time they become obsolete is both resource and time-consuming. Therefore, there is a growing need to allow classification models to detect and adapt to new classes dynamically, without retraining, but instead able to detect new classes using few shots learning [1]. In this paper, we introduce Adaptive Classification By Retrieval CBR, a novel approach for encrypted network traffic classification. Our new approach is based on an ANN-based method, which allows us to effectively identify new and existing classes without retraining the model. The novel approach is simple, yet effective and achieved similar results to RF with up to 5% difference (usually less than that) in the classification tasks while having a slight decrease in the case of new samples (from new classes) without retraining. To summarize, the new method is a real-time classification, which can classify new classes without retraining. Furthermore, our solution can be used as a complementary solution alongside RF or any other machine/deep learning classification method, as an aggregated solution.
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。
