Conventional network access control approaches are static (e.g., user roles in Active Directory), coarse-grained (e.g., 802.1x), or both (e.g., VLANs). Such systems are unable to meaningfully stop or hinder motivated attackers seeking to spread throughout an enterprise network. To address this threat, we present Dynamic Flow Isolation (DFI), a novel architecture for supporting dynamic, fine-grained access control policies enforced in a Software-Defined Network (SDN). These policies can emit and revoke specific access control rules automatically in response to network events like users logging off, letting the network adaptively reduce unnecessary reachability that could be potentially leveraged by attackers. DFI is oblivious to the SDN controller implementation and processes new packets prior to the controller, making DFI's access control resilient to a malicious or faulty controller or its applications. We implemented DFI for OpenFlow networks and demonstrated it on an enterprise SDN testbed with around 100 end hosts and servers. Finally, we evaluated the performance of DFI and how it enables a novel policy, which is otherwise difficult to enforce, that protects against a surrogate of the recent NotPetya malware in an infection scenario. We found that the threat was most limited in its ability to spread using our policy, which automatically restricted network flows over the course of the attack, compared to no access control or a static role-based policy.

Software-defined networking (SDN) continues to grow in popularity because of its programmable and extensible control plane realized through network applications (apps). However, apps introduce significant security challenges that can systemically disrupt network operations, since apps must access or modify data in a shared control plane state. If our understanding of how such data propagate within the control plane is inadequate, apps can co-opt other apps, causing them to poison the control plane's integrity. We present a class of SDN control plane integrity attacks that we call cross-app poisoning (CAP), in which an unprivileged app manipulates the shared control plane state to trick a privileged app into taking actions on its behalf. We demonstrate how role-based access control (RBAC) schemes are insufficient for preventing such attacks because they neither track information flow nor enforce information flow control (IFC). We also present a defense, ProvSDN, that uses data provenance to track information flow and serves as an online reference monitor to prevent CAP attacks. We implement ProvSDN on the ONOS SDN controller and demonstrate that information flow can be tracked with low-latency overheads.

As Software-Defined Networking has gained increasing prominence, new attacks have been demonstrated which can corrupt the SDN controller's view of network topology. These topology poisoning attacks, most notably host-location hijacking and link fabrication attacks, enable adversaries to impersonate end-hosts or inter-switch links in order to monitor, corrupt, or drop network flows. In response, defenses have been developed to detect such attacks and raise an alert. In this paper, we analyze two such defenses, TopoGuard and Sphinx, and present two new attacks, Port Probing and Port Amnesia, that can successfully bypass them. We then develop and present extensions to TopoGuard to make it resilient to such attacks.

Computer security has long been an arms race between attacks and defenses. While new defenses are proposed and built to stop specific vectors of attacks, novel, sophisticated attacks are devised by attackers to bypass them. This rapid cycle of defenses and attacks has made it difficult to strategically reason about the protection offered by each defensive technique, the coverage of a set of defenses, and possible new vectors of attack for which to design future defenses. In this work, we present QUASAR, a framework that systematically analyzes attacks and defenses at the granularity of the capabilities necessary to mount the attacks. We build a model of attacks in the memory corruption domain, and represent various prominent defenses in this domain. We demonstrate that QUASAR can be used to compare defenses at a fundamental level (what they do instead of how they do it), reason about the coverage of a defensive configuration, and hypothesize about possible new attack strategies. We show that of the top five hypothesized new attack strategies, in fact, four have been published in security venues over the past two years. We investigate the fifth hypothesized vector ourselves and demonstrate that it is, in fact, a viable vector of attack.