Security
Open computerized numerical control (CNC) systems, which are crucial pieces of machinery in discrete manufacturing, are under constant security threat. Trusted computing is considered to be an effective way to protect them. However, the machining process of an open CNC system cannot be protected effectively against control-flow hijacking.
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The data set has been prepared as 2 different versions. The data set was shared in two versions due to the fact that the researchers could easily reproduce the tests and hardware limitations. The first version (small_dataset) was prepared using a 10% sub-sample of all dataset. The other version (big_dataset) contains the entire data. In this study, the scenarios tested were run on the small_dataset. The most successful configuration that was selected as a result of the analysis on small_dataset was applied to big_dataset.
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Dataset for evaluation of REMaQE
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SSADLog is a novel log-based anomaly detection framework. It introduces a hyper-efficient log data pre-processing method that generates a representative subset of small sample logs. This is SSADLog pre-processed BGL dataset which are used in training, test1 and test2. You can see the small sample datasets significantly reduce the time required to execute the entire SSADLog framework but still provide a holistic understanding of the original log sequences.
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Cyber Threat Intelligence (CTI) Quality Metrics Introduction
This dataset is part of the respective publication regarding the metrics of CTI quality.
License All datasets are available under a GNUv3 General Public License.
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Internet-of-Things (IoT) technology such as Surveillance cameras are becoming a widespread feature of citizens' life. At the same time, the fear of crime in public spaces (e.g., terrorism) is ever-present and increasing but currently only a small number of studies researched automatic recognition of criminal incidents featuring artificial intelligence (AI), e.g., based on deep learning and computer vision. This is due to the fact that little to none real data is available due to legal and privacy regulations. Consequently, it is not possible to train and test deep learning models.
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This LTE_RFFI project sets up an LTE device radio frequency fingerprint identification system using deep learning techniques. The LTE uplink signals are collected from ten different LTE devices using a USRP N210 in different locations. The sampling rate of the USRP is 25 MHz. The received signal is resampled to 30.72 MHz in Matlab. Then, the signals are processed and saved in the MAT file form. More details about the datasets can be found in the README document.
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5G Network slicing is one of the key enabling technologies that offer dedicated logical resources to different applications on the same physical network. However, a Denial-of-Service (DoS) or Distributed Denial-of-Service (DDoS) attack can severely damage the performance and functionality of network slices. Furthermore, recent DoS/DDoS attack detection techniques are based on the available data sets which are collected from simulated 5G networks rather than from 5G network slices.
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This data set corresponds to Table II:UNIFORMITY OF TC-PUF DESIGN of manuscript titled "A Lightweight and Secure Physical Unclonable Function Design on FPGA". The provided data is for FPGA board No. 1 to 15. Board no. 1 to 14 represent uniformity of 40X40 TC-PUF response implemented on Artix-7 FPGA, and board no. 15 represent uniformity of 20X40 TC-PUF response implemented on Zynq Z-7010 FPGA. It is observed that nearly all the TC-PUF implemented on individual FPGAs have a slight bias towards ‘0’.
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