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Software-defined networking (SDN) has emerged as a flexible network architecture for central and programmatic control. Although SDN can improve network security oversight and policy enforcement, ensuring the security of SDN from sophisticated attacks is an ongoing challenge for practitioners. Existing network forensics tools attempt to identify and track such attacks, but holistic causal reasoning across control and data planes remains challenging. We present PicoSDN, a provenance-informed causal observer for SDN attack analysis. PicoSDN leverages fine-grained data and execution partitioning techniques, as well as a unified control and data plane model, to allow practitioners to efficiently determine root causes of attacks and to make informed decisions on mitigating them. We implement PicoSDN on the popular ONOS SDN controller. Our evaluation across several attack case studies shows that PicoSDN is practical for the identification, analysis, and mitigation of SDN attacks. 

Intent-based networking (IBN) promises to simplify the network management and automated orchestration of high-level policies in future networking architectures such as software-defined networking (SDN). However, such abstraction and automation creates new network visibility challenges. Existing SDN network forensics and diagnostics tools operate at a lower level of network abstraction, which makes intent-level reasoning difficult. We present PROVINTENT, a framework extension for SDN control plane tools that accounts for intent semantics. PROVINTENT records the provenance and evolution of intents as the network’s state and apps’ requests change over time and enables reasoning at multiple abstractions. We define an intent provenance model, we implement a proof-of-concept tool, and we evaluate the efficacy of PROVINTENT’s explanatory capabilities by using a representative intent-driven network application. 

The European Union (EU) General Data Protection Regulation (GDPR) has expanded data privacy regulations regarding personal data for over half a billion EU citizens. Given the regulation’s effectively global scope and its significant penalties for non-compliance, systems that store or process personal data in increasingly complex workflows will need to demonstrate how data were generated and used. In this paper, we analyze the GDPR text to explicitly identify a set of central challenges for GDPR compliance for which data provenance is applicable; we introduce a data provenance model for representing GDPR workflows; and we present design patterns that demonstrate how data provenance can be used realistically to help in verifying GDPR compliance. We also discuss open questions about what will be practically necessary for a provenance-driven system to be suitable under the GDPR. 

Software-defined networking (SDN) achieves a programmable control plane through the use of logically centralized, event-driven controllers and through network applications (apps) that extend the controllers’ functionality. As control plane decisions are often based on the data plane, it is possible for carefully-crafted malicious data plane inputs to direct the control plane towards unwanted states that bypass network security restrictions (i.e., cross-plane attacks). Unfortunately, due to the complex interplay between controllers, apps, and data plane inputs, at present it is difficult to systematically identify and analyze these cross-plane vulnerabilities. We present EventScope, a vulnerability detection tool that automatically analyzes SDN control plane event usage, discovers candidate vulnerabilities based on missing event handling routines, and validates vulnerabilities based on data plane effects. To accurately detect missing event handlers without ground truth or developer aid, we cluster apps according to similar event usage and mark inconsistencies as candidates. We create an event flow graph to observe a global view of events and control flows within the control plane and use it to validate vulnerabilities that affect the data plane. We applied EventScope to the ONOS SDN controller and uncovered 14 new vulnerabilities. 

Software-defined networking (SDN) overcomes many limitations of traditional networking architectures because of its programmable and flexible nature. Security applications, for instance, can dynamically reprogram a network to respond to ongoing threats in real time. However, the same flexibility also creates risk, since it can be used against the network. Current SDN architectures potentially allow adversaries to disrupt one or more SDN system components and to hide their actions in doing so. That makes assurance and reasoning about past network events more difficult, if not impossible. In this paper, we argue that an SDN architecture must incorporate various notions of accountability for achieving systemwide cyber resiliency goals. We analyze accountability based on a conceptual framework, and we identify how that analysis fits in with the SDN architecture’s entities and processes. We further consider a case study in which accountability is necessary for SDN network applications, and we discuss the limits of current approaches 

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.