TWC: TTP Option: Small: Collaborative: SRN: On Establishing Secure and Resilient Networking Services

几乎每个组织都依赖于基于云的服务。云服务的后端是为多个租户设计的，驻留在分布在多个物理位置的数据中心。在这样一个复杂的共享环境中，网络安全和安全管理是主要障碍。这项研究旨在通过采取移动目标防御(MTD)方法来缓解安全挑战。不断调整数据中心的拓扑、带宽分配和流量策略等系统资源，使得攻击者很难危害系统。将制定新的评价方法，以确保这些中期审查机制在实践中正常运作。这项研究的结果是拥有更安全、更具抵御攻击能力的云服务。这项研究是由来自亚利桑那州立大学、杜克大学和密苏里大学堪萨斯城分校三所不同大学的研究人员共同开展的。研究生将接受培训，以满足日益增长的对网络安全专业人员的教育需求。拟议研究的结果将纳入各自机构教授的几门课程。多位置、多租户数据中心环境中的MTD方法需要复杂级别的协调。本研究研究基于可编程网络解决方案的数据中心虚拟网络环境中的防御机制，以便在考虑系统资源消耗、软件错误/漏洞、对策的有效性以及对运行应用程序的消费者的影响的情况下部署主动攻击对策。研究成果可用于需要以非常精细的分辨率(从几毫秒到几秒)准确预测安全态势感知变量的应用程序。这带来了额外的挑战，即为网络、数据收集、支持大数据的安全处理和控制开发新的性能模型。为了应对这些挑战，该项目有两个相互依赖的基础研究推动力：(A)在网络和软件层面研究动态和自适应防御框架；以及(B)部署自适应安全启用的流量工程方法，通过考虑对策的有效性和网络带宽分配来选择最佳对策，同时将对应用的侵入性和部署对策的成本降至最低。该项目的成果将包括一套软件API和工具，用于在过渡到实践的过程中集成测量系统和分析模型。

Almost every organization depends on cloud-based services. The backend of cloud-based services are designed for multiple tenants and reside in data centers spread across multiple physical locations. Network security and security management are major hurdles in such a complex, shared environment. This research investigates mitigating the security challenges by taking a moving target defense (MTD) approach. Continually adjusting the system resources such as the topology of the data center, bandwidth allocation and traffic flow policies makes it difficult for attackers to compromise the system. New evaluations methods will be developed to ensure that these MTD mechanisms work properly in practice. The outcome of this research is to have cloud services that are more secure and resilient to attacks. This research is a collaborative effort conducted by researchers from three different universities, Arizona State University, Duke University, and the University of Missouri-Kansas City. Graduate students will be trained to serve the growing need for educating professionals in cyber security. The results of the proposed research will be incorporated into several courses taught at the respective institutions. The MTD approach in a multi-location, multi-tenant data center environment requires a complex level of coordination. This research investigates defense mechanisms in the data center's virtual networking environment based on programmable networking solutions so that proactive attack countermeasures can be deployed with considerations of the system resource consumption, software bugs/vulnerabilities, effectiveness of countermeasures, and impact on consumers running applications. The research outcomes can be employed for applications that require security situation-awareness variables accurately predicted at a very fine grain resolution, from a few milliseconds to a few seconds. This introduces additional challenges, namely, developing new performance models for networking, data collection, big data-enabled security processing, and control. To address these challenges, this project has two interdependent fundamental research thrusts: (a) investigate a dynamic and adaptive defensive framework at both networking and software levels; and (b) deploy an adaptive security-enabled traffic engineering approach to select optimal countermeasures by considering the effectiveness of countermeasures and network bandwidth allocations while minimizing the intrusiveness to the applications and the cost of deploying the countermeasures. The outcomes of this project will include a set of software APIs and tools to integrate the measurement system and analytical models in a transition to practice effort.