Visible to the public Biblio

Filters: Keyword is Monitoring, Fusion, and Response for Cyber Resilience  [Clear All Filters]
Benjamin E. Ujcich, University of Illinois at Urbana-Champaign, Samuel Jero, MIT Lincoln Laboratory, Anne Edmundson, Princeton University, Qi Wang, University of Illinois at Urbana-Champaign, Richard Skowyra, MIT Lincoln Laboratory, James Landry, MIT Lincoln Laboratory, Adam Bates, University of Illinois at Urbana-Champaign, William H. Sanders, University of Illinois at Urbana-Champaign, Cristina Nita-Rotaru, Northeastern University, Hamed Okhravi, MIT Lincoln Laboratroy.  2018.  Cross-App Poisoning in Software-Defined Networking. 2018 ACM Conference on Computer and Communications Security.

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.

Uttam Thakore, University of Illinois at Urbana-Champaign, Ahmed Fawaz, University of Illinois at Urbana-Champaign, William H. Sanders, University of Illinois at Urbana-Champaign.  2018.  Detecting Monitor Compromise using Evidential Reasoning.

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.

Carmen Cheh, University of Illinois at Urbana-Champaign, Ken Keefe, University of Illinois at Urbana-Champaign, Brett Feddersen, University of Illinois at Urbana-Champaign, Binbin Chen, Advanced Digital Sciences Center Singapre, William G. Temple, Advance Digital Science Center Singapore, William H. Sanders, University of Illinois at Urbana-Champaign.  2017.  Developing Models for Physical Attacks in Cyber-Physical Systems Security and Privacy. ACM Workshop on Cyber-Physical Systems Security and Privacy.

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.