Security

Recent researches have shown that non-sequential tasks based on deep neural networks (DNN), such as image classification and object detection, are vulnerable to backdoor attacks, leading to incorrect model predictions. As a crucial task in computer vision, Scene Text Recognition (STR) is widely used in IoT fields such as intelligent transportation systems and intelligent surveillance. Therefore, a high degree of security is needed to ensure the accuracy of the system for text recognition. However, there are currently no studies on STR backdoor attacks.
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This dataset contains raw FMCW radar signals collected for human localization and activity monitoring in indoor environments. The data was recorded using mmWave radar sensors across two different laboratory settings, designed to simulate real-life scenarios for human detection and localization tasks.
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This dataset contains raw FMCW radar signals collected for human localization and activity monitoring in indoor environments. The data was recorded using mmWave radar sensors across two different laboratory settings, designed to simulate real-life scenarios for human detection and localization tasks.
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This dataset contains raw FMCW radar signals collected for human localization and activity monitoring in indoor environments. The data was recorded using mmWave radar sensors across two different laboratory settings, designed to simulate real-life scenarios for human detection and localization tasks.
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This dataset contains raw FMCW radar signals collected for human localization and activity monitoring in indoor environments. The data was recorded using mmWave radar sensors across two different laboratory settings, designed to simulate real-life scenarios for human detection and localization tasks.
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The CyberAlert-25 Dataset is a comprehensive collection of curated cyber threat data, developed to support advanced research in vulnerability detection, classification, and threat intelligence. Aggregated from authoritative sources such as the National Critical Information Infrastructure Protection Center (NCIIPC) and the MITRE Corporation, the dataset focuses on Common Vulnerabilities and Exposures (CVEs), encompassing a total of 29,650 entries.
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This dataset provides packet traces captured in a realistic 5G Vehicle-to-Everything (5G-V2X) environment, encompassing both legitimate vehicular communications and Distributed Denial of Service (DDoS) attacks. By deploying four user equipments (UEs) under multiple attacker configurations, the collected captures reflect various DDoS types (TCP SYN, UDP, and mixed) and reveal their impact on 5G-V2X networks. The dataset is further enriched with Argus files and CSV feature tables, facilitating data-driven approaches such as Machine Learning (ML)-based detection agents.
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Distributed Denial of Service (DDoS) attacks,
particularly those executed by bots, significantly impair the
Quality of Service (QoS) for legitimate users. While network-level
DDoS attacks have been largely mitigated through decades of
research, application-level DDoS attacks remain a challenge due
to the difficulty in distinguishing malicious from legitimate traffic.
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This dataset is specifically designed to support research related to TIFS(IEEE Transactions on Information Forensics and Security)papers.It offers a comprehensive collection of raw data,extracted feature data,and detailed parameter profiles for the models used in the studies.The raw data includes a wide range of measurements and observations,providing a solid foundation for further analysis.The extracted feature data highlights key characteristics and patterns,making it easier for researchers to identify important trends and insights.Additionally,the detailed parameter profiles offer in-dep
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SNMDat2.0 is a comprehensive multimodal dataset, expanded from the unimodal TwiBot-20, designed for Twitter social bot detection. Specifically, we add 274587 profile images and profile background images, 86498 tweet images and 49549 tweet videos based on the original 229580 twitter users, 227979 follow relationships and 33488192 tweet text.
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