Security
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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The processing of this dataset involves the following steps. First, create a list of file paths that includes the paths of 10 binary files. Then, traverse the files one by one, check if each file exists, and if it does, read the file content.
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We constructed the largest NAS package dataset to date, consisting of 1,489 NAS packages from major third-party sources, which can offer representative data for further research. Given that Synology and QNAP have the largest user bases, these platforms experience the highest frequency of attacks. Furthermore, the NAS package ecosystems provided by other vendors are considerably smaller. So our security measurements focus solely on the NAS packages of Synology and QNAP.
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<p class="MsoNormal"><span style="mso-spacerun: 'yes'; font-family: 宋体; mso-ascii-font-family: Calibri; mso-hansi-font-family: Calibri; mso-bidi-font-family: 'Times New Roman'; font-size: 10.5000pt; mso-font-kerning: 1.0000pt;"><span style="font-family: Calibri;">This dataset contains expert evaluations of various text features using Grey Relational Analysis (GRA), comparing the performance of original and new prompt words.
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Cloud computing has become a relatively new paradigm for the delivery of compute re-
sources, with key management services (KMS) playing a crucial role in securely handling cryptographic
operations in the cloud. This paper presents the microbenchmark of cloud cryptographic workloads, in-
cluding SHA HMAC generation, AES encryption/decryption, ECC signature/verification, and RSA encryp-
tion/decryption, across Function-as-a-Service (FaaS) and Infrastructure-as-a-Service (IaaS) in conjunction
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The Drone Sensor Fusion Dataset features high-quality telemetry data from real and attack-modeled UAV flights, leveraging the PX4 flight log dataset. This includes normal flight data prepared for machine learning model training and simulated attack data generated using the 'Coordinated Sensor Manipulation Attack' (CSMA) model. CSMA simulates advanced threats by subtly altering GPS and IMU data to induce undetectable navigation drift.
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420 Views
This paper presents an enhanced methodology for network anomaly detection in Industrial IoT (IIoT) systems using advanced data aggregation and Mutual Information (MI)-based feature selection. The focus is on transforming raw network traffic into meaningful, aggregated forms that capture crucial temporal and statistical patterns. A refined set of 150 features including unique IP counts, TCP acknowledgment patterns, and ICMP sequence ratios was identified using MI to enhance detection accuracy.
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407 Views
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.
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172 Views
Modern power systems face growing risks from cyber-physical attacks, necessitating enhanced resilience due to their societal function as critical infrastructures. The challenge is that defense of large-scale systems-of-systems requires scalability in their threat and risk assessment environment for cyber-physical analysis including cyber-informed transmission planning, decision-making, and intrusion response. Hence, we present a scalable discrete event simulation tool for analysis of energy systems, called DESTinE.
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