In this work , we study the problem of allocating limited security countermeasures to protect network data from cyber-attacks, for scenarios modeled by Bayesian attack graphs.
We consider multi-stage interactions between a network administrator and cybercriminals, formulated as a security game.
We propose parameterized heuristic strategies for the attacker and defender and provide detailed analysis of their time complexity.
Our heuristics exploit the topological structure of attack graphs and employ sampling methods to overcome the computational complexity in predicting opponent actions.
Due to the complexity of the game, we employ a simulation-based approach and perform empirical game analysis over an enumerated set of heuristic strategies.
Finally, we conduct experiments in various game settings to evaluate the performance of our heuristics in defending networks, in a manner that is robust to uncertainty about the security environment.
The data file is comprised of 22,500 X-ray projections (15 scans of 1500 projections each) recorded during solidification of Al-Ge-Na. The raw data file is in .hdf format and can be reconstructed into .tiff, e.g., by using the TomoPy toolbox in Python.
Moniri, S., Xiao, X., & Shahani, A. J. (2019). The mechanism of eutectic modification by trace impurities. Scientific Reports, 9(1), 3381. https://doi.org/10.1038/s41598-019-40455-3
This data is part of a large program to translate detection and interpretation of HFOs into clinical use. A zip file is included which contains hfo detections, metadata, and Matlab scripts. The matlab scripts analyze this input data and produce figures as in the referenced paper (note: the blind source separation method is stochastic, and so the figures may not be exactly the same). A file "README.txt" provides more detail about each individual file within the zip file.
This is a large scale, long-term autonomy dataset for robotics research collected on the University of Michigan’s North Campus. The dataset consists of omnidirectional imagery, 3D lidar, planar lidar, GPS, and proprioceptive sensors for odometry collected using a Segway robot. The dataset was collected to facilitate research focusing on longterm autonomous operation in changing environments. The dataset is comprised of 27 sessions spaced approximately biweekly over the course of 15 months. The sessions repeatedly explore the campus, both indoors and outdoors, on varying trajectories, and at different times of the day across all four seasons. This allows the dataset to capture many challenging elements including: moving obstacles (e.g., pedestrians, bicyclists, and cars), changing lighting, varying viewpoint, seasonal and weather changes (e.g., falling leaves and snow), and long-term structural changes caused by construction projects. To further facilitate research, we also provide ground-truth pose for all sessions in a single frame of reference. and A detailed description of the dataset and the methods used to generate it is in the document nclt.pdf. If you use this dataset in your research please cite:
Carlevaris-Bianco, N., Ushani, A., Eustice, R. (2021). The University of Michigan North Campus Long-Term Vision and LIDAR Dataset [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/7rnm-6a03
Carlevaris-Bianco, Nicholas, et al. “University of Michigan North Campus Long-Term Vision and Lidar Dataset.” The International Journal of Robotics Research, vol. 35, no. 9, Aug. 2016, pp. 1023–1035, doi:10.1177/0278364915614638.
The data contained in the file comprises those collected during the characterization of the sensor as described in the article "Investigation of a low-cost magneto-inductive magnetometer for space science applications" (cited below). This includes:, Resolution
, Stability
, Linearity
, and Frequency response
This archive contains data files from spark-ignited homogeneous combustion internal combustion engine experiments. Included are high-resolution two-dimensional two-component velocity fields acquired at two 5 x 6 mm regions, one near the head and one near the piston. Crank angle resolved heat flux measurements were made at a third location in the head. The engine was operated at 40 kPa, 500 and 1300 RPM, motor and fired. Included are in-cylinder pressure measurements, external pressure and temperature data, as well as details on the geometry of the optical engine to enable setups of simulation configurations.
This data is in support of the publication in review "Using sensor data to dynamically map large-scale models to site-scale forecasts: A case study using the National Water Model". It is all the raw data extracted from the NWM flow forecasts for Iowa and the IFIS stage readings.
For the NWM data, each date has it's own tab-delimited file with columns being the time (hrs) and rows being the NHD site.
For the IFIS gages, each tab delimited file is for a single site for the period of record.
Each pdf is an electronic version of the paper output for each experiment.
Each text file is the electronic version of the data on the computer cards for each experiment. These text files are directly readable by Excel. Once in Excel, the data can be manipulated as desired.
Additional information is in the theses.
This data set contains the relevant time series for constructing and testing electricity load models within the related paper. The files within are a '.mat' file that contains the data and a 'readme.txt' file detailing the contents of the data.
This archive contains data files from spark-ignited homogenous combustion internal combustion engine experiments. Included are two-dimensional two-component velocity fields from various measurement planes with maximized field of view, in-cylinder pressure measurements, external pressure and temperature data, as well as details on the geometry of the optical engine to enable setups of simulation configurations. Fired operation was with stoichiometric propane air, 40kPa MAP, at 1300 RPM.