标题： 面向深度联邦的云生态系统恶意软件防御方法 显示英文标题

标题： Towards Deep Federated Defenses Against Malware in Cloud Ecosystems

摘要： 在拥有众多虚拟机、容器及其他系统的云计算环境中，恶意软件的大规模爆发对业务流程构成了极大的威胁。本文是一篇展望性论文，我们介绍了一种分层方法，利用图、超图以及自然语言处理领域的一些最新机器学习进展，来进行恶意软件的检测与分析。我们分析单个系统及其日志，通过注意力机制序列模型来检查和理解它们的行为。基于这一过程中每套系统日志的特征表示，我们构建了一个以系统和其他组件为顶点的云环境属性网络，并提出使用归纳图及超图学习模型来分析恶意软件。在此基础上，我们考虑了多云场景，即多个具有不同隐私要求的云协作以遏制恶意软件的传播，建议使用联邦学习来执行推理和训练，同时保护隐私。最后，我们讨论了在防御云计算环境中的恶意软件方面仍存在的几个开放问题，包括设计稳健生态系统、识别云特定的响应策略优化问题、恶意软件遏制和根除的动作空间，以及为该领域的机器学习模型开发先验知识和迁移学习任务。 显示英文摘要

摘要： In cloud computing environments with many virtual machines, containers, and other systems, an epidemic of malware can be highly threatening to business processes. In this vision paper, we introduce a hierarchical approach to performing malware detection and analysis using several recent advances in machine learning on graphs, hypergraphs, and natural language. We analyze individual systems and their logs, inspecting and understanding their behavior with attentional sequence models. Given a feature representation of each system's logs using this procedure, we construct an attributed network of the cloud with systems and other components as vertices and propose an analysis of malware with inductive graph and hypergraph learning models. With this foundation, we consider the multicloud case, in which multiple clouds with differing privacy requirements cooperate against the spread of malware, proposing the use of federated learning to perform inference and training while preserving privacy. Finally, we discuss several open problems that remain in defending cloud computing environments against malware related to designing robust ecosystems, identifying cloud-specific optimization problems for response strategy, action spaces for malware containment and eradication, and developing priors and transfer learning tasks for machine learning models in this area.