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  1. Free, publicly-accessible full text available July 1, 2023
  2. Internet-of-things (IoT) introduce new attack surfaces for power grids with the usage of Wi-Fi enabled high wattage appliances. Adversaries can use IoT networks as a foothold to significantly change load demands and cause physical disruptions in power systems. This new IoT-based attack makes current security mechanisms, focusing on either power systems or IoT clouds, ineffective. To defend the attack, we propose to use a data-centric edge computing infrastructure to host defense mechanisms in IoT clouds by integrating physical states in decentralized regions of a power grid. By enforcing security policies on IoT devices, we can significantly limit the range ofmore »malicious activities, reducing the impact of IoT-based attacks. To fully understand the impact of data-centric edge computing on IoT clouds and power systems, we developed a cyber-physical testbed simulating six different power grids. Our preliminary results show that performance overhead is negligible, with less than 5% on average.« less
  3. Reconnaissance is critical for adversaries to prepare attacks causing physical damage in industrial control systems (ICS) like smart power grids. Disrupting the reconnaissance is challenging. The state-of-the-art moving target defense (MTD) techniques based on mimicking and simulating system behaviors do not consider the physical infrastructure of power grids and can be easily identified. To overcome those challenges, we propose physical function virtualization (PFV) that ``hooks'' network interactions with real physical devices and uses them to build lightweight virtual nodes following the actual implementation of network stacks, system invariants, and physical state variations of real devices. On top of PFV, wemore »propose DefRec, a defense mechanism that significantly increases the reconnaissance efforts for adversaries to obtain the knowledge of power grids' cyber-physical infrastructures. By randomizing communications and crafting decoy data for the virtual physical nodes, DefRec can mislead adversaries into designing damage-free attacks. We implement PFV and DefRec in the ONOS network operating system and evaluate them in a cyber-physical testbed, which uses real devices from different vendors and HP physical switches to simulate six power grids. The experiment results show that with negligible overhead, PFV can accurately follow the behavior of real devices. DefRec can significantly delay passive attacks for at least five months and isolate proactive attacks with less than $10^{-30}$ false negatives.« less
  4. Reconnaissance is critical for adversaries to prepare attacks causing physical damage in industrial control systems (ICS) like smart power grids. Disrupting reconnaissance is challenging. The state-of-the-art moving target defense (MTD) techniques based on mimicking and simulating system behaviors do not consider the physical infrastructure of power grids and can be easily identified. To overcome these challenges, we propose physical function virtualization (PFV) that “hooks” network interactions with real physical devices and uses these real devices to build lightweight virtual nodes that follow the actual implementation of network stacks, system invariants, and physical state variations in the real devices. On topmore »of PFV, we propose DefRec, a defense mechanism that significantly increases the effort required for an adversary to infer the knowledge of power grids’ cyber-physical infrastructures. By randomizing communications and crafting decoy data for virtual nodes, DefRec can mislead adversaries into designing damage-free attacks. We implement PFV and DefRec in the ONOS network operating system and evaluate them in a cyber-physical testbed, using real devices from different vendors and HP physical switches to simulate six power grids. The experimental results show that with negligible overhead, PFV can accurately follow the behavior of real devices. DefRec can delay adversaries’ reconnaissance for more than 100 years by adding a number of virtual nodes less than or equal to 20% of the number of real devices.« less
  5. We present a computer interface to visualize and interact with mathematical knots, i.e., the embeddings of closed circles in 3-dimensional Euclidean space. Mathematical knots are slightly different than everyday knots in that they are infinitely stretchy and flexible when being deformed into their topological equivalence. In this work, we design a visualization interface to depict mathematical knots as closed node-link diagrams with energies charged at each node, so that highly-tangled knots can evolve by themselves from high-energy states to minimal (or lower) energy states. With a family of interactive methods and supplementary user interface elements, out tool allows one tomore »sketch, edit, and experiment with mathematical knots, and observe their topological evolution towards optimal embeddings. In addition, out interface can extract from the entire knot evolution those key moments where successive terms in the sequence differ by critical change; this provides a clear and intuitive way to understand and trace mathematical evolution with a minimal number of visual frames. Finally out interface is adapted and extended to support the depiction of mathematical links and braids, whose mathematical concepts and interactions are just similar to our intuition about knots. All these combine to show a mathematically rich interface to help us explore and understand a family of fundamental geometric and topological problems.« less