Security
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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A simulated data set. The simulation data set sets up some parameter change association relationships and randomly assigns them to related parameters.
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This dataset is in support of my research paper '9-DoF Personal Robot Servant'.
Preprint :
On this robot servant, my first work was done in the year 2011.
Image Source: https://www.dreamstime.com/robot-tray-d-rendering-mini-holding-serving-i...
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Distributed Denial of Service (DDoS) attacks first appeared in the mid-1990s, as attacks stopping legitimate users from accessing specific services available on the Internet. A DDoS attack attempts to exhaust the resources of the victim to crash or suspend its services. Time series modeling will help system administrators for better planning of resource allocation to defend against DDoS attacks. Different Time Series analysis techniques are applied to detect the DDoS attacks.
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