The need for improved network situational awareness has been highlighted by the growing complexity and severity of cyber-attacks. Mobile phones pose a significant risk to network situational awareness due to their dynamic behaviour and lack of visibility on a network. Machine learning techniques enhance situational awareness by providing administrators insight into the devices and activities which form their network. Developing machine learning techniques for situational awareness requires a testbed to generate and label network traffic. Current testbeds, however, are unable to automate the generation and labelling of realistic network traffic. To address this, we describe a testbed which automates applications on mobile devices to generate and label realistic traffic. From this testbed, two labelled datasets of network traffic have been created. We provide an analysis of the testbed automation reliability and benchmark the datasets for the task of application classification.

翻译：网络攻击的日益复杂化和严重性凸显了提升网络态势感知能力的必要性。移动电话因其动态行为及网络可见性不足，对网络态势感知构成重大风险。机器学习技术通过向管理员提供构成其网络的设备与活动洞察，可增强态势感知能力。开发用于态势感知的机器学习技术需要能够生成并标注网络流量的测试平台，然而现有测试平台无法自动生成和标注真实的网络流量。为解决此问题，本文描述了一种能够自动化操作移动设备应用以生成并标注真实流量的测试平台。基于该平台，我们创建了两个带标注的网络流量数据集，并对该平台的自动化可靠性进行了分析，同时针对应用分类任务对数据集进行了基准测试。