NSL KDD V2

The NSL-KDD V2 is an extended version of NSL-KDD original dataset. The dataset is normalised and 1 additional class is synthesised by mixing multiple non-benign classes. 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. 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.NSL-KDD V2是原始NSL-KDD数据集的扩展版本。该数据集已完成归一化处理，并通过混合多个非良性类别合成了1个新增类别。 若需引用该数据集，请参考其原创论文，DOI为：10.1109/SmartNets61466.2024.10577645。该论文发表于IEEE SmartNets会议，可在此处获取。 引用详情： Madhubalan, Akshayraj、Gautam, Amit与Tiwary, Priya. (2024). Blender-GAN：用于新类别合成数据生成的多目标条件生成对抗网络（Generative Adversarial Network，GAN）. 1-7. 10.1109/SmartNets61466.2024.10577645. 本数据集由总部位于帕洛阿尔托的研究型数据保护企业Abluva Inc.开发。我司数据保护平台可通过细粒度访问控制、先进的去个性化算法（如伪匿名化（Pseudonymization）、匿名化（Anonymization）与随机化（Randomization））等高级安全机制，助力客户实现数据安全防护。Abluva的数据保护解决方案可推动组织内外的数据民主化，缓解数据盗窃与合规相关顾虑。Abluva创新性的入侵检测算法采用专利技术，通过精细平衡的检测逻辑排除正常访问偏差，确保在不干扰业务运营的前提下实现入侵检测。Abluva的解决方案还可帮助企业通过安全知识图谱（Knowledge Graphs）、安全数据即服务（Secure Data as a Service）等创新数据应用场景，进一步挖掘数据价值。Abluva始终致力于打造安全可靠的环境，助力企业充分释放数据的全部潜能。