Generating user activity is a key capability for both evaluating security monitoring tools as well as improving the credibility of attacker analysis platforms (e.g., honeynets). In this paper, to generate this activity, we instrument each machine by means of an external agent. This agent combines both deterministic and deep learning based methods to adapt to different environment (e.g., multiple OS, software versions, etc.), while maintaining high performances. We also propose conditional text generation models to facilitate the creation of conversations and documents to accelerate the definition of coherent, system-wide, life scenarios.
翻译:生成用户活动是评价安全监测工具以及提高攻击者分析平台(如蜂窝网)可信度的关键能力,在本文中,为了产生这种活动,我们通过外部代理器对每台机器进行仪器操作,该代理器结合基于确定性和深层次学习的方法,以适应不同的环境(如多种OS、软件版本等),同时保持高性能,我们还提议有条件的文本生成模型,以便利创建谈话和文件,加速界定全系统范围的一致性生活设想。