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CIF: Small: Adversarial Network Tomography: Inferring Network State from Manipulated End-to-End Measurements

CIF：小型：对抗性网络断层扫描：从操纵的端到端测量推断网络状态

基本信息

批准号：
1813219
负责人：
Ting He
金额：
$ 17万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813219&HistoricalAwards=false
关键词：
CIF Small Adversarial Network Tomography

项目摘要

An accurate and timely view of a network's internal state (e.g., distributions of link delays/jitters/losses or various statistics of these distributions) is at the heart of many network management functions, such as traffic engineering, service placement, and fault detection/localization. Obtaining such a view has, however, become more challenging than ever in modern computer communication networks such as the Internet, hybrid optical/copper networks, future cellular networks, and distributed cloud networks, due to their increased complexity and heterogeneity. The traditional network monitoring approach that is based on pervasively deployed monitoring agents (e.g., SNMP) or pervasively supported network protocols (e.g., traceroute) faces severe limitations in such complex and heterogeneous environments. Network tomography, which aims at inferring the network internal state from end-to-end measurements taken from the peripheral of the network, provides a powerful alternative approach that can construct a view of the internal state without directly monitoring the internal links/nodes. Existing network tomography solutions, however, assume that all the internal nodes behave consistently in traffic forwarding, which makes them vulnerable in an adversarial setting, where certain nodes can manipulate the traffic traversing them to alter the end-to-end measurements. This project will investigate the vulnerability of existing network tomography solutions in an adversarial setting and develop guidelines for defense mechanisms. The primary objective of the project is to quantify the vulnerability of existing network tomography algorithms through rigorous vulnerability analysis, which involves actually developing the optimal attack strategy for each representative tomography algorithm and analyzing its impact in terms of the maximum performance degradation that an adversary can cause without being detected/localized. Concrete optimization problems will be formulated and solved for network tomography algorithms designed for different types of network states, including additive metrics that represent link delay/loss statistics, min metrics that represent available link capacities, and Boolean metrics that represent link congestion/failure states. Based on the vulnerability analysis, insights will be drawn on the reliability of tomography-based network monitoring in adversarial environments, and guidelines will be developed for future network tomography algorithms. The proposed research is grounded on latest advances on network tomography in the benign setting, including state estimation algorithms, measurement design algorithms, and theory about their limitations and performance. The research will be performed at the intersection of optimization and algorithm design, involving linear/non-linear optimization, combinatorial optimization, parameter estimation, and empirical validations. The project will provide training experience for students, some from underrepresented groups, through participation in the theoretical study, implementation of algorithms, and conduct of empirical validations based on real datasets from the Internet.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

准确及时地查看网络的内部状态（例如，链路延迟/抖动/损耗的分布或这些分布的各种统计数据）是许多网络管理功能（例如流量工程、服务布置和故障检测/定位）的核心。然而，在现代计算机通信网络（诸如互联网、混合光/铜网络、未来蜂窝网络和分布式云网络）中，由于其增加的复杂性和异构性，获得这样的视图变得比以往更具挑战性。传统的网络监控方法是基于普遍部署的监控代理（例如，SNMP）或普遍支持的网络协议（例如，跟踪路由）在这种复杂和异构的环境中面临严重的限制。网络断层扫描，其目的是推断网络的内部状态从端到端的测量从网络的外围设备，提供了一个强大的替代方法，可以构建一个内部状态的视图，而不直接监视内部链路/节点。然而，现有的网络断层扫描解决方案假设所有内部节点在流量转发中的行为一致，这使得它们在对抗性设置中容易受到攻击，其中某些节点可以操纵穿过它们的流量以改变端到端测量。该项目将调查现有网络断层扫描解决方案在对抗环境中的脆弱性，并制定防御机制的指导方针。该项目的主要目标是通过严格的脆弱性分析，量化现有网络断层扫描算法的脆弱性，其中包括实际开发每个代表性的断层扫描算法的最佳攻击策略，并分析其在最大性能下降方面的影响，对手可以导致不被检测/本地化。具体的优化问题将制定和解决网络断层扫描算法设计的不同类型的网络状态，包括添加剂的指标，代表链路延迟/丢失统计，最小的指标，代表可用的链路容量，和布尔指标，代表链路拥塞/故障状态。在脆弱性分析的基础上，将对基于断层扫描的网络监控在对抗环境中的可靠性提出见解，并为未来的网络断层扫描算法制定指导方针。所提出的研究是基于网络断层扫描在良性环境中的最新进展，包括状态估计算法，测量设计算法，以及它们的局限性和性能的理论。该研究将在优化和算法设计的交叉点进行，涉及线性/非线性优化，组合优化，参数估计和经验验证。该项目将通过参与理论研究、算法实施以及基于互联网上的真实的数据集进行实证验证，为学生（其中一些来自代表性不足的群体）提供培训经验。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。