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2017
Zhou, Wenxuan, Croft, Jason, Liu, Bingzhe, Caesar, Matthew.  2017.  NEAt: Network Error Auto-Correct. Proceedings of the Symposium on SDN Research. :157–163.

Configuring and maintaining an enterprise network is a challenging and error-prone process. Administrators must often consider security policies from a variety of sources simultaneously, including regulatory requirements, industry standards, and to mitigate attack vectors. Erroneous implementation of a policy, however, can result in costly data breaches and intrusions. Relying on humans to discover and troubleshoot violations is slow and prone to error, considering the speed at which new attack vectors propagate and the increasing network dynamics, partly an effect of SDN. To ensure the network is always in a state consistent with the desired policies, administrators need frameworks to automatically diagnose and repair violations in real-time. To address this problem, we present NEAt, a system analogous to a smartphone's autocorrect feature that enables on-the-fly repair to policy-violating updates. NEAt modifies the forwarding behavior of updates to automatically repair violations of properties such as reachability, service chaining, and segmentation. NEAt sits between an SDN controller and the forwarding devices, and intercepts updates proposed by SDN applications. If an update violates the policy defined by an administrator, such as reachability or segmentation, NEAt transforms the update into one that complies with the policy. Unlike domain-specific languages or synthesis platforms, NEAt allows enterprise networks to leverage the advanced functionality of SDN applications while simultaneously achieving strong, automated enforcement of general policies.

2014
Das, Anupam, Borisov, Nikita, Caesar, Matthew.  2014.  Analyzing an Adaptive Reputation Metric for Anonymity Systems. Proceedings of the 2014 Symposium and Bootcamp on the Science of Security. :11:1–11:11.

Low-latency anonymity systems such as Tor rely on intermediate relays to forward user traffic; these relays, however, are often unreliable, resulting in a degraded user experience. Worse yet, malicious relays may introduce deliberate failures in a strategic manner in order to increase their chance of compromising anonymity. In this paper we propose using a reputation metric that can profile the reliability of relays in an anonymity system based on users' past experience. The two main challenges in building a reputation-based system for an anonymity system are: first, malicious participants can strategically oscillate between good and malicious nature to evade detection, and second, an observed failure in an anonymous communication cannot be uniquely attributed to a single relay. Our proposed framework addresses the former challenge by using a proportional-integral-derivative (PID) controller-based reputation metric that ensures malicious relays adopting time-varying strategic behavior obtain low reputation scores over time, and the latter by introducing a filtering scheme based on the evaluated reputation score to effectively discard relays mounting attacks. We collect data from the live Tor network and perform simulations to validate the proposed reputation-based filtering scheme. We show that an attacker does not gain any significant benefit by performing deliberate failures in the presence of the proposed reputation framework.