UNSW-NB15 V3

The dataset is an extended version of UNSW-NB 15. It has 1 additional class synthesised and the data is normalised for ease of use. To cite the dataset, please reference the original paper with DOI: 10.1109/SmartNets61466.2024.10577645. The paper is published in IEEE SmartNets and can be accessed here: https://www.researchgate.net/publication/382034618_Blender-GAN_Multi-Target_Conditional_Generative_Adversarial_Network_for_Novel_Class_Synthetic_Data_Generation. Citation info: Madhubalan, Akshayraj & Gautam, Amit & Tiwary, Priya. (2024). Blender-GAN: Multi-Target Conditional Generative Adversarial Network for Novel Class Synthetic Data Generation. 1-7. 10.1109/SmartNets61466.2024.10577645. This dataset was made by Abluva Inc, a Palo Alto based, research-driven Data Protection firm. Our data protection platform empowers customers to secure data through advanced security mechanisms such as Fine Grained Access control and sophisticated depersonalization algorithms (e.g. Pseudonymization, Anonymization and Randomization). Abluva's Data Protection solutions facilitate data democratization within and outside the organizations, mitigating the concerns related to theft and compliance. The innovative intrusion detection algorithm by Abluva employs patented technologies for an intricately balanced approach that excludes normal access deviations, ensuring intrusion detection without disrupting the business operations. Abluva’s Solution enables organizations to extract further value from their data by enabling secure Knowledge Graphs and deploying Secure Data as a Service among other novel uses of data. Committed to providing a safe and secure environment, Abluva empowers organizations to unlock the full potential of their data.本数据集为UNSW-NB 15的扩展版本，新增1个合成类别，并对数据进行了归一化处理以提升易用性。 如需引用本数据集，请参考其原始论文，该论文的DOI为10.1109/SmartNets61466.2024.10577645，发表于IEEE SmartNets会议，可通过以下链接获取：https://www.researchgate.net/publication/382034618_Blender-GAN_Multi-Target_Conditional_Generative_Adversarial_Network_for_Novel_Class_Synthetic_Data_Generation。 引用信息： Madhubalan, Akshayraj、Gautam, Amit与Tiwary, Priya.（2024）. Blender-GAN：面向新颖类别合成数据生成的多目标条件生成对抗网络（Multi-Target Conditional Generative Adversarial Network），页码1-7，DOI：10.1109/SmartNets61466.2024.10577645。 本数据集由位于美国帕洛阿尔托的研究型数据保护企业Abluva Inc.开发。其数据保护平台依托细粒度访问控制（Fine Grained Access Control）、高级去个性化算法（如假名化（Pseudonymization）、匿名化（Anonymization）、随机化（Randomization））等先进安全机制，助力客户保障数据安全。Abluva的数据保护解决方案可推动组织内外的数据民主化，缓解数据窃取及合规性相关的顾虑。Abluva研发的创新型入侵检测算法采用多项专利技术，通过精细平衡的检测逻辑排除正常访问偏差，在保障入侵检测有效性的同时不会干扰企业业务运营。Abluva的解决方案还可通过构建安全知识图谱（Knowledge Graph）、部署安全数据即服务（Secure Data as a Service）等创新数据应用方式，助力企业从其数据中挖掘更多潜在价值。Abluva始终致力于打造安全可靠的环境，助力企业充分释放其数据的全部潜力。

搜集汇总

背景与挑战

背景概述

UNSW-NB15 V3是UNSW-NB15网络入侵检测数据集的扩展版本，发布于2024年。其主要特点包括新增了一个合成类别，并对数据进行了归一化处理，以提高数据在机器学习任务中的易用性。该数据集适用于计算机与信息科学领域的研究，特别是网络安全和异常检测。