Security
Cyber-Physical Production Systems (CPPS) are the key enabling for industrial businesses and economic growth. The introduction of the Internet of Things (IoT) in industrial processes represents a new Internet revolution, mostly known as 4th Industrial Revolution, towards the Smart Manufacturing concept. Despite the huge interest from the industry side to innovate their production systems, in order to increase revenues at lower costs, the IoT concept is still immature and fuzzy, which increases security related risks in industrial systems.
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A simple dataset that gives the processing cost (in cycles) for verifying multiple messages signed with ECDSA and implicitly certified public keys. It considers two implicit certification models: ECQV and SIMPL.
This dataset is used in article "Schnorr-based implicit certification: improving the security and efficiency of vehicular communications", submitted to IEEE Transactions on Computers. Namely, it is used as basis for building that article's Figure 2.
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GPS spoofing and jamming are common attacks against the UAV, however, conducting these experiments for research can be difficult in many areas. This dataset consists of a logs from simulation and live fights. Logs include data from a benign flight as well as one where the UAV experiences an attack.
Simulated Attacks
Note: This is not the most accurate of the data. We recommend using the live GPS Spoofing and Jamming data if possible.
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Data of an attack example in Bit-Level DFA of SM4
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This data was used for the paper: J. Wang, P. Aubry, and A. Yarovoy, " Three-Dimensional Short-Range Imaging With Irregular MIMO Arrays Using NUFFT-Based Range Migration Algorithm", IEEE Transactions on Geoscience and Remote Sensing. It includes two synthetic electromagnetic datasets and one experimental measured data with multiple-input-multiple-output (MIMO) arrays.
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The users graph structure of Sina network.
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Information:
This dataset was created for research on blockchain anomaly and fraud detection. And donated to IEEE data port online community.
https://github.com/epicprojects/blockchain-anomaly-detection
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This is a dataset consisting of 8 features extracted from 70,000 monochromatic still images adapted from the Genome Project Standford's database, that are labeled in two classes: LSB steganography (1) and without LSB Steganography (0). These features are Kurtosis, Skewness, Standard Deviation, Range, Median, Geometric Mean, Hjorth Mobility, and Hjorth Complexity, all extracted from the histograms of the still images, including random spatial transformations. The steganographic function embeds five types of payloads, from 0.1 to 0.5.
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