地球资源数据云——数据资源详情
UGRansome 数据集
该数据集《UGRansome dataset》主要用于多分类任务，数据形态以图像为主，应用场景偏向医疗健康。 题目说明：A dataset for anomaly detection in zero - day attacks and ransomware 任务类型：图像多分类。 建议流程：先检查类别分布与脏样本，再用迁移学习（如 ResNet/EfficientNet）建立基线。 注意事项：疑似存在类别不均衡，建议使用分层抽样、类别权重与 F1/Recall 指标。 可用文件：final(2).csv。 The UGRansome dataset is a versatile cybersecurity resource designed for the analysis of ransomware and zero - day cyber - attacks, particularly those exhibiting cyclostationary behavior. This dataset features various essential components, including timestamps for attack time tracking, flags for categorizing attack types, protocol data for understanding attack vectors, network flow details to observe data transfer patterns, and ransomware family classifications. It also provides insight into the associated malware, numeric clustering for pattern recognition, and quantifies financial damage in both USD and bitcoins (BTC). The dataset employs machine learning to generate attack signatures and offers synthetic signatures for testing and simulating cybersecurity defenses. Additionally, it can be used to identify and document anomalies, contributing to anomaly detection research and enhancing cybersecurity understanding and preparedness. This dataset offers valuable information for researchers and practitioners interested in leveraging it for various analytical and investigatory purposes such as ransomware and zero - day threats detection and classification. The dataset required deduplication and transformation.
摘要概览
该数据集《UGRansome dataset》主要用于多分类任务，数据形态以图像为主，应用场景偏向医疗健康。 题目说明：A dataset for anomaly detection in zero - day attacks and ransomware
任务类型：图像多分类。
建议流程：先检查类别分布与脏样本，再用迁移学习（如 ResNet/EfficientNet）建立基线。
注意事项：疑似存在类别不均衡，建议使用分层抽样、类别权重与 F1/Recall 指标。
可用文件：final(2).csv。
The UGRansome dataset is a versatile cybersecurity resource designed for the analysis of ransomware and zero - day cyber - attacks, particularly those exhibiting cyclostationary behavior.
This dataset features various essential components, including timestamps for attack time tracking, flags for categorizing attack types, protocol data for understanding attack vectors, network flow details to observe data transfer patterns, and ransomware family classifications.
It also provides insight into the associated malware, numeric clustering for pattern recognition, and quantifies financial damage in both USD and bitcoins (BTC). The dataset employs machine learning to generate attack signatures and offers synthetic signatures for testing and simulating cybersecurity defenses.
Additionally, it can be used to identify and document anomalies, contributing to anomaly detection research and enhancing cybersecurity understanding and preparedness.
This dataset offers valuable information for researchers and practitioners interested in leveraging it for various analytical and investigatory purposes such as ransomware and zero - day threats detection and classification. The dataset required deduplication and transformation.