Data Quality for Security Challenges: Case Studies of Phishing, Malware and Intrusion Detection Datasets

Data Quality for Security Challenges: Case Studies of Phishing, Malware and Intrusion Detection Datasets
复制标题

DOI：
10.1145/3319535.3363267
复制
发表时间：
2019-11
期刊：
Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security
影响因子：
--
通讯作者：
Rakesh M. Verma;Victor Zeng;Houtan Faridi
Rakesh M. Verma;Victor Zeng;Houtan Faridi
中科院分区：
其他
文献类型：
--
作者：
Rakesh M. Verma;Victor Zeng;Houtan Faridi

文献摘要

被引文献

相似文献

数据科学的技术越来越多地被研究人员应用于安全挑战。然而，安全领域特有的挑战需要对模型的有效性和鲁棒性进行细致的处理。在本文中，我们解释了与安全相关的数据质量的关键维度，用网络钓鱼、入侵检测和恶意软件的几个流行数据集来说明它们，指出了确保数据质量的操作方法，并试图激励读者为安全挑战生成高质量的数据集。
Techniques from data science are increasingly being applied by researchers to security challenges. However, challenges unique to the security domain necessitate painstaking care for the models to be valid and robust. In this paper, we explain key dimensions of data quality relevant for security, illustrate them with several popular datasets for phishing, intrusion detection and malware, indicate operational methods for assuring data quality and seek to inspire the audience to generate high quality datasets for security challenges.