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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SNS

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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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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Penetration testing plays an important role in securing websites. However, you need the right tools to run efficient tests. Penetration testing tools have different functions, pentest methodologies, features, and price ranges. It might be difficult to choose the ones most suitable for your organization. This post will briefly describe some of the finest penetration testing tools.

 

 

 

 

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The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules.

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Industrial Internet of Things (IIoTs) are high-value cyber targets due to the nature of the devices and connectivity protocols they deploy. They are easy to compromise and, as they are connected on a large scale with high-value data content, the compromise of any single device can extend to the whole system and disrupt critical functions. There are various security solutions that detect and mitigate intrusions.

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