论文标题
主动与被动：网络协议的自动机学习范例的比较
Active vs. Passive: A Comparison of Automata Learning Paradigms for Network Protocols
论文作者
论文摘要
主动自动机学习成为通信协议行为分析的流行工具。主要优点是，由于行为模型是从黑框系统自动推断的，因此无需手动建模工作。但是，该技术的几种现实应用程序表明，建立主动接口的开销可能会妨碍实际的适用性。我们最近在积极学习蓝牙低能（BLE）方案的工作发现，积极的相互作用在学习过程中会产生瓶颈。考虑到自动机学习工具集，被动学习技术似乎是一种有前途的解决方案，因为它们不需要在学习中的系统中有主动的接口。相反，基于给定的数据集学习模型。在本文中，我们评估了两个网络协议的被动学习：BLE和消息排队遥测运输（MQTT）。我们的结果表明，被动技术可以使用比主动学习所需的数据更少的数据正确学习。但是，与主动学习成本相比，用于被动学习的一般随机数据生成更昂贵。
Active automata learning became a popular tool for the behavioral analysis of communication protocols. The main advantage is that no manual modeling effort is required since a behavioral model is automatically inferred from a black-box system. However, several real-world applications of this technique show that the overhead for the establishment of an active interface might hamper the practical applicability. Our recent work on the active learning of Bluetooth Low Energy (BLE) protocol found that the active interaction creates a bottleneck during learning. Considering the automata learning toolset, passive learning techniques appear as a promising solution since they do not require an active interface to the system under learning. Instead, models are learned based on a given data set. In this paper, we evaluate passive learning for two network protocols: BLE and Message Queuing Telemetry Transport (MQTT). Our results show that passive techniques can correctly learn with less data than required by active learning. However, a general random data generation for passive learning is more expensive compared to the costs of active learning.
