Obtained domain name blacklist substring N-gram features.

NIAID Data Ecosystem2026-03-14 收录

恶意域名检测

网络安全特征工程

数据链接：

https://figshare.com/articles/dataset/Obtained_domain_name_blacklist_substring_N-gram_features_/21969066 数据链接链接失效反馈

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资源简介：

Obtained domain name blacklist substring N-gram features.

创建时间：

2023-01-27

Gravity Falls

恶意域名检测

短信钓鱼

Gravity Falls是由达科他州立大学团队构建的半合成DGA检测数据集，涵盖2022至2025年通过短信钓鱼（smishing）传播的恶意域名。该数据集包含4个技术集群共4万条数据，记录威胁行为者从随机字符串到词典拼接及主题组合抢注的技术演进。数据源自真实短信链接、开源情报及基础设施分析，采用混合方法（直接观测与正则预测）构建，主要用于评估传统启发式与机器学习模型在移动端钓鱼域名检测中的泛化

arXiv2026-03-04 更新250

Benign and Malicious Domains with Lexical and Active Features for Machine Learning-Based Detection

恶意域名检测

DNS分析

Dataset Composition: Examples: 200,000 Benign: 100,000 Malicious: 100,000 Features: 28 Dataset composed of stratified random samples of benign domains derived from the Majestic Million list and malic

NIAID Data Ecosystem50

DGA and Legitimate DNS Domains

恶意域名检测

DGA域名生成算法

Dataset used in "Selvi, J., Rodríguez, R. J., & Soria-Olivas, E. (2019). Detection of algorithmically generated malicious domain names using masked N-grams. Expert Systems with Applications, 124, 156-

NIAID Data Ecosystem40

DGA and Legitimate Domain names samples for malicious DGA detection experiments with transformer-based embeddings

恶意域名检测

DGA分类

Datasets used to evaluate a proposed DGA detection approach using Integrated transformer embeddings from large language models.The original full dataset is available from:GitHub - chrmor/DGA_domains_d

DataCite Commons2025-12-14 更新150

Benign and malicious domains based on DNS logs

恶意域名检测

监督学习

The dataset is meant for supervised machine learning based analysis of malicious and non-malicious domain names. The dataset was created from scratch, using publicly DNS logs of both malicious and n

NIAID Data Ecosystem40

Obtained domain name blacklist substring N-gram features.

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