Advanced Persistent Threat (APT) Classified Dataset

In deep learning, images are utilized due to their rich information content, spatial hierarchies, and translation invariance, rendering them ideal for tasks such as object recognition and classification. The classification of malware using images is an important field for deep learning, especially in cybersecurity. Within this context, the Classified Advanced Persistent Threat Dataset is a thorough collection that has been carefully selected to further this field's study and innovation. This dataset comprises distinct subsets: one containing samples attributed to twelve prominent APT groups, and another cataloging yearly APT samples spanning from 2011 to 2023. Each subset offers a unique insight into cyber threats, providing researchers with diverse opportunities for analysis and exploration. Employing the innovative Ahash technique, the samples are intricately categorized into subclasses, laying the groundwork for in-depth study and investigation. With a primary focus on advancing malware classification methodologies, particularly through image-based deep learning approaches, this dataset serves as a vital resource for fortifying cybersecurity defenses against the evolving landscape of cyber threats.

在深度学习领域中，图像凭借其丰富的信息含量、空间层级结构与平移不变性特性得以广泛应用，成为目标识别与分类等任务的理想载体。基于图像的恶意软件分类是深度学习在网络安全领域的重要研究方向。在此背景下，分类化高级持续性威胁（Classified Advanced Persistent Threat, APT）数据集是一套经过精心甄选的高质量数据集，旨在推动该领域的研究与技术创新。该数据集包含两个独立子集：其一涵盖12个顶尖APT组织的样本，其二则收录了2011年至2023年的年度APT样本。每个子集均能从独特视角揭示网络威胁的特征，为研究人员提供多元化的分析与探索空间。本数据集采用创新性的Ahash技术，将样本细致划分为若干子类，为深入研究与分析奠定了基础。本数据集以优化恶意软件分类方法为核心目标，尤其聚焦于基于图像的深度学习分类方案，可作为强化网络安全防御、应对不断演变的网络威胁态势的关键资源。

搜集汇总

背景与挑战

背景概述

这是一个专注于高级持续性威胁（APT）恶意软件分类的数据集，旨在通过基于图像的深度学习方法提升网络安全防御。数据集包含两个子集：一个涵盖12个主要APT组织的样本，另一个按年度（2011-2023）组织APT样本，并使用Ahash技术进行细粒度子分类，以支持恶意软件识别和分析研究。