Security

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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Electric power systems are comprised of cyber and physical components that are crucial to grid resiliency. Data from both components should be collected when modeling power systems: data from communication networks and intrusion detection systems; physical telemetry from sensors and field devices.

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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 and is saved in the MAT file form. The corresponding processed signals are included in the dataset. More details about the datasets can be found in the README document.

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The dataset, titled "SensorNetGuard: A Dataset for Identifying Malicious Sensor Nodes," comprises 10,000 samples with 21 features. It is designed to facilitate the identification of malicious sensor nodes in a network environment, specifically focusing on IoT-based sensor networks.

General Metrics

§  Node ID: The unique identifier for each node.

§  Timestamp: The time at which data or a packet is sent or received.

§  IP Address: Internet Protocol address of the node.

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Physically unclonable functions (PUFs) are foundational components that offer a cost-efficient and promising solution for diverse security applications, including countering integrated circuit (IC) counterfeiting, generating secret keys, and enabling lightweight authentication. PUFs exploit semiconductor variations in ICs to derive inherent responses from imposed challenges, creating unique challenge-response pairs (CRPs) for individual devices. Analyzing PUF security is pivotal for identifying device vulnerabilities and ensuring response credibility.

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Iman Sharafaldin et al. generated the real time network traffic and these are made available at the Canadian Institute of Cyber security Institute website.  The team of researchers published the network traffic data and has made the dataset publicly available in both PCAP and CSV formats. The network traffic data is generated during two days. Training Day was on January 12th, 2018 and Testing Day was on March 11th, 2018.

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It is a challenging work to solve the geometric attack in the field of digital watermarking. In order to solve the synchronization between the host image and the watermark, image normalization is introduced. Firstly, the geometrically invariant space of image is constructed by using image normalization, and a region of interest (ROI) is obtained from the normalized image by utilizing the invariant centroid theory. Then, the contourlet transform is performed on the ROI. Low-pass sub-band coefficients are divided into non-overlapping blocks.

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The data set downloaded from Wikimedia Download is available as a test data set related to the key-value structure.Searchable encryption schemes that need to implement keyword search can use this dataset as test data.Wikimedia Commons is a media repository of free-to-use images, sounds, videos and other media.

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