Biblio

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.

Stealthy attackers often disable or tamper with system monitors to hide their tracks and evade detection. In this poster, we present a data-driven technique to detect such monitor compromise using evidential reasoning. Leveraging the fact that hiding from multiple, redundant monitors is difficult for an attacker, to identify potential monitor compromise, we combine alerts from different sets of monitors by using Dempster-Shafer theory, and compare the results to find outliers. We describe our ongoing work in this area.

In this paper, we analyze the security of cyber-physical systems using the ADversary VIew Security Evaluation (ADVISE) meta modeling approach, taking into consideration the efects of physical attacks. To build our model of the system, we construct an ontology that describes the system components and the relationships among them. The ontology also deines attack steps that represent cyber and physical actions that afect the system entities. We apply the ADVISE meta modeling approach, which admits as input our deined ontology, to a railway system use case to obtain insights regarding the system’s security. The ADVISE Meta tool takes in a system model of a railway station and generates an attack execution graph that shows the actions that adversaries may take to reach their goal. We consider several adversary proiles, ranging from outsiders to insider staf members, and compare their attack paths in terms of targeted assets, time to achieve the goal, and probability of detection. The generated results show that even adversaries with access to noncritical assets can afect system service by intelligently crafting their attacks to trigger a physical sequence of efects. We also identify the physical devices and user actions that require more in-depth monitoring to reinforce the system’s security.

Many of the emerging wide-area monitoring protection and control (WAMPAC) applications in modern electrical grids rely heavily on the availability and integrity of widespread phasor measurement unit (PMU) data. Therefore, it is critical to protect PMU networks against growing cyber-attacks and system faults. In this paper, we present a self-healing PMU network design that considers both power system observability and communication network characteristics. Our design utilizes centralized network control, such as the emerging software-defined networking (SDN) technology, to design resilient network self-healing algorithms against cyber-attacks. Upon detection of a cyber-attack, the PMU network can reconfigure itself to isolate compromised devices and re-route measurement
data with the goal of preserving the power system observability. We have developed a proof-of-concept system in a container-based network testbed using integer linear programming to solve a graphbased PMU system model.We also evaluate the system performance regarding the self-healing plan generation and installation using the IEEE 30-bus system.
 

State estimation is a fundamental problem for monitoring and controlling systems. Engineering systems interconnect sensing and computing devices over a shared bandwidth-limited channels, and therefore, estimation algorithms should strive to use bandwidth optimally. We present a notion of entropy for state estimation of switched nonlinear dynamical systems, an upper bound for it and a state estimation algorithm for the case when the switching signal is unobservable. Our approach relies on the notion of topological entropy and uses techniques from the theory for control under limited information. We show that the average bit rate used is optimal in the sense that, the efficiency gap of the algorithm is within an additive constant of the gap between estimation entropy of the system and its known upper-bound. We apply the algorithm to two system models and discuss the performance implications of the number of tracked modes.

In distributed control systems with shared resources, participating agents can improve the overall performance of the system by sharing data about their personal references. In this paper, we formulate and study a natural tradeoff arising in these problems between the privacy of the agent’s data and the performance of the control system.We formalize privacy in terms of differential privacy of agents’ preference vectors. The overall control system consists of N agents with linear discrete-time coupled dynamics, each controlled to track its preference vector. Performance of the system is measured by the mean squared tracking error. We present a mechanism that achieves differential privacy by adding Laplace noise to the shared information in a way that depends on the sensitivity of the control system to the private data. We show that for stable systems the performance cost of using this type of privacy preserving mechanism grows as O(T³ /Nε²), where T is the time horizon and ε is the privacy parameter. For unstable systems, the cost grows exponentially with time. From an estimation point of view, we establish a lower-bound for the entropy of any unbiased estimator of the private data from any noise-adding mechanism that gives ε-differential privacy. We show that the mechanism achieving this lower-bound is a randomized mechanism that also uses Laplace noise.

Enterprise networks today have highly diverse correctness requirements and relatively common performance objectives. As a result, preferred abstractions for enterprise networks are those which allow matching correctness specification, while transparently managing performance. Existing SDN network management architectures, however, bundle correctness and performance as a single abstraction. We argue that this creates an SDN ecosystem that is unnecessarily hard to build, maintain and evolve. We advocate a separation of the diverse correctness abstractions from generic performance optimization, to enable easier evolution of SDN controllers and platforms. We propose Oreo, a first step towards a common and relatively transparent performance optimization layer for SDN. Oreo performs the optimization by first building a model that describes every flow in the network, and then performing network-wide, multi-objective optimization based on this model without disrupting higher level correctness.

Software defined networking is a rapidly expanding networking paradigm that aims to separate the control logic from the forwarding devices. Through centralized control, network operators are able to deploy and manage more efficient forwarding strategies. Traditionally, when the network undergoes a change through maintenance, failure, or cyber attack, the centralized controller processes these events and deploys new forwarding rules reactively. This work provides a strategy that does not require a controller in order to maintain connectivity while only using features within the existing OpenFlow protocol version 1.3 or greater. In this paper we illustrate why forwarding resiliency is desired in OpenFlow networks and provide an algorithm that computes the flow entries required to achieve maximal forwarding resiliency in presence of both multiple link and controller failures on any arbitrary network.

Attack graphs used in network security analysis are analyzed to determine sequences of exploits that lead to successful acquisition of privileges or data at critical assets. An attack graph edge corresponds to a vulnerability, tacitly assuming a connection exists and tacitly assuming the vulnerability is known to exist. In this paper we explore use of uncertain graphs to extend the paradigm to include lack of certainty in connection and/or existence of a vulnerability. We extend the standard notion of uncertain graph (where the existence of each edge is probabilistically independent) however, as signicant correlations on edge existence probabilities exist in practice, owing to common underlying causes for dis-connectivity and/or presence of vulnerabilities. Our extension describes each edge probability as a Boolean expression of independent indicator random variables. This paper (i) shows that this formalism is maximally descriptive in the sense that it can describe any joint probability distribution function of edge existence, (ii) shows that when these Boolean expressions are monotone then we can easily perform uncertainty analysis of edge probabilities, and (iii) uses these results to model a partial attack graph of the Stuxnet worm and a small enterprise network and to answer important security-related questions in a probabilistic manner.

This article describes our recent progress on the development of rigorous analytical metrics for assessing the threat-performance trade-off in control systems. Computing systems that monitor and control physical processes are now pervasive, yet their security is frequently an afterthought rather than a first-order design consideration. We investigate a rational basis for deciding—at the design level—how much investment should be made to secure the system.

Although computational systems are looking towards post CMOS devices in the pursuit of lower power, the expected inherent unreliability of such devices makes it difficult to design robust systems without additional power overheads for guaranteeing robustness. As such, algorithmic structures with inherent ability to tolerate computational errors are of significant interest. We propose to cast applications as stochastic algorithms based on Markov chains (MCs) as such algorithms are both sufficiently general and tolerant to transition errors. We show with four example applications—Boolean satisfiability, sorting, low-density parity-check decoding and clustering—how applications can be cast as MC algorithms. Using algorithmic fault injection techniques, we demonstrate the robustness of these implementations to transition errors with high error rates. Based on these results, we make a case for using MCs as an algorithmic template for future robust low-power systems.

It is critical to ensure that network policy remains consistent during state transitions. However, existing techniques impose a high cost in update delay, and/or FIB space. We propose the Customizable Consistency Generator (CCG), a fast and generic framework to support customizable consistency policies during network updates. CCG effectively reduces the task of synthesizing an update plan under the constraint of a given consistency policy to a verification problem, by checking whether an update can safely be installed in the network at a particular time, and greedily processing network state transitions to heuristically minimize transition delay. We show a large class of consistency policies are guaranteed by this greedy jeuristic alone; in addition, CCG makes judicious use of existing heavier-weight network update mechanisms to provide guarantees when necessary. As such, CCG nearly achieves the “best of both worlds”: the efficiency of simply passing through updates in most cases, with the consistency guarantees of more heavyweight techniques. Mininet and physical testbed evaluations demonstrate CCG’s capability to achieve various types of consistency, such as path and bandwidth properties, with zero switch memory overhead and up to a 3× delay reduction compared to previous solutions.

Workarounds to computer access in healthcare are sufficiently common that they often go unnoticed. Clinicians focus on patient care, not cybersecurity. We argue and demonstrate that understanding workarounds to healthcare workers’ computer access requires not only analyses of computer rules, but also interviews and observations with clinicians. In addition, we illustrate the value of shadowing clinicians and conducing focus groups to understand their motivations and tradeoffs for circumvention. Ethnographic investigation of the medical workplace emerges as a critical method of research because in the inevitable conflict between even well-intended people versus the machines, it’s the people who are the more creative, flexible, and motivated. We conducted interviews and observations with hundreds of medical workers and with 19 cybersecurity experts, CIOs, CMIOs, CTO, and IT workers to obtain their perceptions of computer security. We also shadowed clinicians as they worked. We present dozens of ways workers ingeniously circumvent security rules. The clinicians we studied were not “black hat” hackers, but just professionals seeking to accomplish their work despite the security technologies and regulations.