Security
ZJT datasets: It was collected from the production line of China Tobacco Zhejiang Industrial Company. The data was sampled every two seconds for a week from 162 sensors deployed on a variety of production devices (e.g., paper cut-ting wheel, power supply, etc.). Since ZJT is a dataset from real-world production line, it does not contain serious anoma-lies from accidents or attacks. Thus, we treat the states of transforming between different producing modes as anoma-lies. The ratio of normal states to abnormal states is 4:1.
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The PermGuard dataset is a carefully crafted Android Malware dataset that maps Android permissions to exploitation techniques, providing valuable insights into how malware can exploit these permissions. It consists of 55,911 benign and 55,911 malware apps, creating a balanced dataset for analysis. APK files were sourced from AndroZoo, including applications scanned between January 1, 2019, and July 1, 2024. A novel construction method extracts Android permissions and links them to exploitation techniques, enabling a deeper understanding of permission misuse.
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Here is a dataset for our paper RED-Scenario: A Resource-Efficient Deployment Framework for Scenarios through Dependency Package Management
Dependency and Size Knowledge Graphs for 10979 Python packages with 597,049 versions, and 28,151 Node.js packages with 738,927 versions, each version containing size and dependency information.
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The necessity for strong security measures to fend off cyberattacks has increased due to the growing use of Industrial Internet of Things (IIoT) technologies. This research introduces IoTForge Pro, a comprehensive security testbed designed to generate a diverse and extensive intrusion dataset for IIoT environments. The testbed simulates various IIoT scenarios, incorporating network topologies and communication protocols to create realistic attack vectors and normal traffic patterns.
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The coperfed dataset, which contains four datasets:ICS traffic data, low-interaction ICS honeypot data, high-interaction ICS honeypot data, attack tools data. ICS traffic data is collected in a simulated power system with two MTUs and six RTUs, and the traffic includes six types of attack traffic as well as regular system polling and manual operation traffic. The low-interaction ICS honeypot data are traffic from 10 different attacking organizations collected by a Modbus honeypot developed by from July 2017 to February 2023.
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This document presents the probability calculation for distinguishing Hamming weight distributions. Given two distinct Hamming weight scenarios (HW = 0 and HW = 1), we derive the probability that an observed value \(x\) originates from the HW = 0 distribution. Utilizing Bayes' theorem, we calculate the conditional probability and provide the final expression for \(P(HW = 0 \mid x)\).
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The dataset is based on PYTHON and machine modeling of 5000 challenge response pairs for both regular CRO PUFs and HMCRO PUFs.This includes the design of reordering schemes
The dataset is based on PYTHON and machine modeling of 5000 challenge response pairs for both regular CRO PUFs and HMCRO PUFs.This includes the design of reordering schemes
The dataset is based on PYTHON and machine modeling of 5000 challenge response pairs for both regular CRO PUFs and HMCRO PUFs.This includes the design of reordering schemes
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We propose a deep learning-based dataset for smart contract vulnerability detection, combining three public datasets to support and facilitate blockchain security research. This comprehensive dataset includes a variety of common types of smart contract vulnerabilities, such as re-entrancy attacks, integer overflows, and improper access controls.
By consolidating and uniformly annotating the data, we provide detailed vulnerability information and classification tags for each smart contract. The main features and contributions of the dataset are as follows:
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The Unified Multimodal Network Intrusion Detection System (UM-NIDS) dataset is a comprehensive, standardized dataset that integrates network flow data, packet payload information, and contextual features, making it highly suitable for machine learning-based intrusion detection models. This dataset addresses key limitations in existing NIDS datasets, such as inconsistent feature sets and the lack of payload or time-window-based contextual features.
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DALHOUSIE NIMS LAB BENIGN DATASET 2024-2 dataset comprises data captured from Consumer IoT devices, depicting three primary real-life states (Power-up, Idle, and Active) experienced by everyday users. Our setup focuses on capturing realistic data through these states, providing a comprehensive understanding of Consumer IoT devices.
The dataset comprises of nine popular IoT devices namely
Amcrest Camera
Smarter Coffeemaker
Ring Doorbell
Amazon Echodot
Google Nestcam
Google Nestmini
Kasa Powerstrip
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