Security
Human facial data hold tremendous potential to address a variety of classification problems, including face recognition, age estimation, gender identification, emotion analysis, and race classification. However, recent privacy regulations, such as the EU General Data Protection Regulation, have restricted the ways in which human images may be collected and used for research.
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Dynamic malicious software detection aims to assess whether executable programs exhibit malicious behavior by thoroughly studying and analyzing their dynamic features. However, many current methodologies insufficiently explore the semantic features of API sequences and instead rely more on mining parameter information during API call processes to enhance detection performance. This leads to issues such as excessive dependence on prior knowledge, larger model parameter sizes, and higher computational complexities.
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The Illinois 200-Bus Cyber-Physical System (CPS) serves as an important case for studying risk analysis through the integration of physical and cyber components. This system designed to reflect real-world architectures. Access Complexity scores are assigned to its network edges based on the Common Vulnerability Scoring System (CVSS) Access Complexity (AC), allowing for the simulation of adversarial pathways within the system.
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A real world radio frequency fingerprinting (RFF) dataset for enhancement strategy by exploiting the physical unclonable function (PUF) to tune the RF hardware impairments in a unique and secure manner, which is exemplified by taking power amplifiers (PAs) in RF chains as an example. This is achieved by intentionally and slightly tuning the PA non-linearity characteristics using the active load-pulling technique. The dataset is collected from the cable-connected measurement and over-the-air measurement.
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This dataset presents real-world IoT device traffic captured under a scenario termed "Active," reflecting typical usage patterns encountered by everyday users. Our methodology emphasizes the collection of authentic data, employing rigorous testing and system evaluations to ensure fidelity to real-world conditions while minimizing noise and irrelevant capture.
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The Army Cyber Institute (ACI) Internet of Things (IoT) Network Traffic Dataset 2023 (ACI-IoT-2023) is a novel dataset tailored for machine learning (ML) applications in the realm of IoT network security. This effort focuses on delivering a distinctive and realistic dataset designed to train and evaluate ML models for IoT network environments. By addressing a gap in existing resources, this dataset aims to propel advancements in ML-based solutions, ultimately fortifying the security of IoT operations.
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We propose a more challenging dataset known as Weibo23. By amalgamating all available fake news from the Weibo Management Community until March 2023 with existing samples from public datasets [1], we formed a comprehensive collection of fake news for Weibo23. Fabricated news articles were thoroughly examined and authenticated by certified experts.
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this is a dataset for Human-Robot Physical Contact Classification. We used the UR10e six-axis robotic arm as the data collection object and the official tool, RTDE, as the data acquisition tool. Regarding the labels of the dataset, we categorize Human-Robot physical contact into three types: no contact, intentional contact, and collision, based on common occurrences in Human-Robot collaborative tasks. The dataset contains 2375 non-repetitive data entries with valid Human-Robot physical contact information, and each entry includes the motion data of the robotic arm within 1 second.
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Captcha stands for Completely Automated Public Turing Tests to Distinguish Between Humans and Computers. This test cannot be successfully completed by current computer systems; only humans can. It is applied in several contexts for machine and human identification. The most common kind found on websites are text-based CAPTCHAs.A CAPTCHA is made up of a series of alphabets or numbers that are linked together in a certain order.
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Smart contract vulnerabilities have led to substantial disruptions, ranging from the DAO attack
to the recent Poolz Finance arithmetic overflow incident. While historically, the definition of smart contract
vulnerabilities lacked standardization, even with the current advancements in Solidity smart contracts, the
potential for deploying malicious contracts to exploit legitimate ones persists.
The abstract Syntax Tree (AST ), Opcodes, and Control Flow Graph (CFG) are the intermediate representa-
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