Security
This is the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99 The Fifth International Conference on Knowledge Discovery and Data Mining. The competition task was to build a network intrusion detector, a predictive model capable of distinguishing between bad'' connections, called intrusions or attacks, andgood'' normal connections. This database contains a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment.
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Six vector maps are selected as experimental data. These maps are referred to as the coastline map, river map, building map, green land map, road map and waterway map, as shown in Fig. 9. The coastline map and river map, both in the China Geodetic Coordinate System 2000 (CGCS2000), can be downloaded from the website of the National Catalogue Service for Geographic Information of China (https://www.webmap.cn).
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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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