Security

each application has up to two files. One for memory dataset and another for control flow dataset. Each dataset is composed of JSON objects. Each instruction is a JSON object.

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A dataset of Global Positioning System (GPS) spoofing attacks is presented in this article. This dataset includes data extracted from authentic GPS signals collected from different locationsto emulate a moving and a static autonomous vehicle using a universal software radio peripheral unit configured as a GPS receiver. During the data collection, 13 features are extracted from eight-parallel channels at different receiver stages (i.e., acquisition, tracking, and navigation decoding).

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Video is a sequence of pictures, which are taken by camera at a short interval of time. A picture in video is called as frame, and the number of frames per second is defined as the frame rate, which denotes the temporal resolution of video. With the higher frame rate, the video contains the more details, such as to the improvement of visual quality for human interpretation or the fine representation for automatic machine perception. A high frame rate relies on the hardware configuration of camera, the higher the frame rate, the more expensive hardware devices.

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The dataset provided contains the invariants in the form of antecedent and consequent mined using Association Rule Mining with implicit measures such as Confidence, Support, Lift, etc. from an EPIC Plant (Electrical Power and Intelligent Control System).

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MulVAL Interaction Rules and their mapping to MITRE ATT&CK

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Privacy Policies from Six Smart Speaker Manufacturers

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Each dataset is splitted by trainset, devset and testset.

Please read them with pytorch.

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Physical unclonable functions (PUFs) are a strong and secure root source for identification and authentication applications. PUFs are especially valuable in FPGA-based systems because FPGA designs are vulnerable to intellectual property (IP) thefts and cloning, which PUFs protect against by generating random but device-specific bitstrings. Theoretically, the randomness of PUFs originates from variations in the manufacturing process. PUFs should be free of deterministic variation owing to the systematic bias among all chips of the same model.

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This dataset is used in the experiment of the paper "A Data Embedding Scheme for Efficient Program Behavior Modeling with Neural Networks" accepted by IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI). System calsl and their relevant branch sequences are contained in the tar.gz file. For a detailed description, please refer to the paper.

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This is an accurately labeled dataset designed to support foreign bodies detection research in industrial production scenarios. In order to facilitate the reproduction of the results of the original paper, we provide this dataset for further research.

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