Collaborative Research: SaTC: CORE: Small: Bankrupting Attackers in Dynamic Networks

在动态计算机网络中，参与者可以自由地加入和离开，几乎不受网络的管理控制，同时享有显著的匿名性。 这使得动态网络容易受到Sybil攻击，其中对手将自己伪装成多个参与者以破坏网络。许多Sybil防御采用资源燃烧（RB），这是网络资源的可验证支出，如计算能力，计算机内存或带宽。不幸的是，现有的方法需要合法的参与者不断执行RB，无论是否发生攻击。该项目的目标是设计新的可扩展的Sybil防御系统;也就是说，在没有恶意行为的情况下，RB的数量很低，并且随着对手发起攻击所花费的资源而缓慢增长。这项研究的成果有可能保护广泛的动态网络，如内容共享系统，电子商务评论平台和公共访问服务器设置。该项目将以两个研究讲习班的形式促进网络安全从业人员和学术界之间的合作。 该项目的目标还与课程开发相结合，沿着本科生和研究生的研究机会。该项目涉及三个一般应用领域：安全对等网络的有效组维护;电子商务审查系统中的垃圾邮件防御;以及基于云的减少拒绝服务攻击的方法。在每一个领域，防御将被设计为以可扩展的合法参与者的RB成本来保护关键的安全不变量：随着攻击者支付的RB成本和合法参与者加入和离开系统的速度而缓慢增长。为了实现这一目标，该项目利用了一个新的框架，估计对抗活动的数量，然后根据这一估计向参与者收取RB成本。该框架可以利用现有的机器学习（ML）结果来估计对抗活动。重要的是，这些防御提供的保证可以抵御持续攻击，即使是ML错误。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

In dynamic computer networks, participants may freely join and depart with little administrative control by the network, while enjoying significant anonymity. This makes dynamic networks vulnerable to the Sybil attack, where an adversary misrepresents itself as multiple participants in order to disrupt the network. Many Sybil defenses employ resource burning (RB), which is the verifiable expenditure of a network resource, such as computing power, computer memory, or bandwidth. Unfortunately, existing approaches require legitimate participants to constantly perform RB, regardless of whether an attack is occurring. The goal of this project is to design new Sybil defenses that are scalable; that is, the amount of RB is low in the absence of malicious behavior, and grows slowly as a function of the resources expended by the adversary to launch its attack. The outcomes of this research have the potential to secure a broad range of dynamic networks, such as content-sharing systems, e-commerce review platforms, and public-access server settings. This project will foster collaboration between cybersecurity practitioners and academia in the form of two research workshops. The objectives of this project are also integrated with curriculum development, along with research opportunities for both undergraduate and graduate students.Three general application areas are addressed under this project: efficient group maintenance for secure peer-to-peer networks; defending against spam in e-commerce review systems; and cloud-based methods for mitigating denial-of-service attacks. In each area, defenses will be designed that protect critical security invariants at an RB cost to legitimate participants that is scalable: growing slowly with both the RB cost paid by an attacker and the rate at which legitimate participants join and depart the system. To achieve this, the project leverages a novel framework, whereby the amount of adversarial activity is estimated, and then participants are charged an RB cost based on this estimate. This framework can leverage existing machine learning (ML) results to estimate adversarial activity. Importantly, the guarantees provided by these defenses hold over persistent attacks, even with ML error. The theoretical components of this research effort will be complemented by empirical evaluations of the proposed defenses.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.