Network traffic classification that is widely applicable and highly accurate is valuable for many network security and management tasks. A flexible and easily configurable classification framework is ideal, as it can be customized for use in a wide variety of networks. In this paper, we propose a highly configurable and flexible machine learning traffic classification method that relies only on statistics of sequences of packets to distinguish known, or approved, traffic from unknown traffic. Our method is based on likelihood estimation, provides a measure of certainty for classification decisions, and can classify traffic at adjustable certainty levels. Our classification method can also be applied in different classification scenarios, each prioritizing a different classification goal. We demonstrate how our classification scheme and all its configurations perform well on real-world traffic from a high performance computing network environment.

翻译：对于许多网络安全和管理任务来说,广泛适用和高度准确的网络交通分类对许多网络安全和管理任务来说是有价值的。一个灵活和容易配置的分类框架是理想的,因为它可以定制,供各种网络使用。在本文中,我们建议采用一种高度可配置和灵活的机器学习交通分类方法,仅依靠数据包序列的序列统计,以区分已知或核准的交通与未知交通。我们的方法基于可能性估计,为分类决定提供了一定程度的确定性,并且可以按可调整的确定性水平对交通进行分类。我们的分类方法也可以适用于不同的分类方案,每个分类方案都优先考虑不同的分类目标。我们展示我们的分类计划及其所有配置如何在实际世界交通中从高性能计算网络环境中运行良好。