Security

Advanced Metering Infrastructure is prone to cyber threats and Distributed Denial of Service is renowned attack that threatens critical infrastructure. Intrusion detection schemes proposed in earlier works consider the network specific parameters for intrusion detection rather than physical layer parameters. Hence, the proposal considers the physical layer parameters in detecting distributed denial of service attack through abnormal energy consumption pattern of the Data concentrator in the advanced metering infrastructure.

This dataset contains Challenge-Response Pairs (CRPs) from Ring Oscillator-based Physical Unclonable Functions (RO-PUFs) operating under constant and variable voltage conditions. Collected from three chips fabricated in a 22nm FDSOI process and powered by a Switched-Capacitor DC-DC converter, it includes two sections: responses at a fixed voltage and across nine voltage levels (nominal and ±10% variations). Organized into ten folders, the dataset includes 1K CRPs per chip for each voltage and a combined folder with 20K CRPs.

The data is derived from 22,898 comments on driverless and human driving obtained by crawler technology on China's Weibo and XiaoHongshu platforms from May 1 to August 31, 2024. The main data formats are xlsx, py, txt, json and so on. The files in py format are script files, which are used to process data. The dataset was eventually used for topic mining, sentiment analysis, and more on Chinese users' comments on driverless and human driving.

During the course of this experimental study, we meticulously collected and recorded a comprehensive set of data. These data not only reflect the precise outcomes of the experimental procedures but also directly correspond to the contents presented in the tables within the research paper. These results are crucial for validating our research hypotheses, providing a solid quantitative foundation for our understanding and analysis of the experimental phenomena.

This paper explores the cryptanalysis of the ASCON algorithm, a lightweight cryptographic method designed for applications like the Internet of Things (IoT). We utilize deep learning techniques to identify potential vulnerabilities within ASCON's structure. First, we provide an overview of how ASCON operates, including key generation and encryption processes.

This paper explores the cryptanalysis of the ASCON algorithm, a lightweight cryptographic method designed for applications like the Internet of Things (IoT). We utilize deep learning techniques to identify potential vulnerabilities within ASCON's structure. First, we provide an overview of how ASCON operates, including key generation and encryption processes.

This dataset supports the research on hybrid quantum encryption by providing simulation results for Quantum Bit Error Rate (QBER) vs. Channel Loss in Quantum Key Distribution (QKD). The dataset includes numerical values used to generate the QBER vs. Channel Loss graph, which illustrates how increasing channel loss impacts quantum encryption performance. This relationship is illustrated graphically by the uploaded dataset image, which shows the critical threshold at which QBER surpasses secure transmission limits.

The AMD3IR dataset is a large-scale collection of Shortwave Infrared (SWIR) and Longwave Infrared (LWIR) images, designed to advance the ongoing research in the field of drone detection and tracking. It efficiently addresses key challenges such as detecting and distinguishing small airborne objects, differentiating drones from background clutter, and overcoming visibility limitations present in conventional imaging. The dataset comprises 20,865 SWIR images with 24,994 annotated drones and 8,696 LWIR images with 10,400 annotated drones, featuring various UAV models.

DLSF is the first dedicated dataset for Text-Image Synchronization Forgery (TISF) in multimodal media. The source data for this dataset is scraped from the Chinese news aggregation platform, Toutiao. This dataset includes extensive text, image, and audio-video data from news articles involving politicians and celebrities, featuring samples of both entity-level and attribute-level TISF. It provides comprehensive annotations, including labels for text-image authenticity, types of TISF, image forgery regions, and text forgery tokens.

The Metaverse Gait Authentication Dataset (MGAD) is a large-scale gait dataset designed for biometric authentication in virtual environments. It contains gait data from 5,000 simulated users, generated in Unity 3D and processed using OpenPose and MediaPipe to extract 16 key features, including stride length, step frequency, joint angles, ground reaction forces, and gait symmetry index.