CRII: SaTC: Towards Data-effective and Cost-efficient Security Attack Detections

设备和人员与互联网的连接不断增加，造成了不断扩大的安全攻击面。机器学习（ML）技术已被用于帮助检测攻击，并可能提供一种更具可扩展性的方法来处理越来越大的攻击面。然而，获取大量高质量的标记攻击样本既昂贵又耗时。此外，所获取的数据集通常不能完全代表真实的数据分布。鉴于标记数据稀缺和代表性不平衡的挑战，该项目的创新之处在于探索构建数据驱动的网络攻击检测系统的新方法，这些系统可以以具有成本效益的方式从有限或有偏见的网络数据集中有效地学习。该项目更广泛的意义和重要性是：1）增强数据驱动的安全攻击检测基础设施，使网络空间更加安全和可信; 2）通过创建鼓励真实的安全产品的开源系统，弥合研究与实践之间的差距，3）为AI领域的本科生和研究生提供研究机会/该项目揭示了如何将有限和/或不平衡的攻击样本用作有效的训练数据，以促进数据驱动的模型构建，并在实践中以低成本实现高性能的安全攻击检测。为了实现这一见解，该项目包含三种技术方法：（1）跨模态对抗性重编程，通过向输入插入补丁级扰动来重新使用先前训练的Transformer模型，减少所需的参数数量，但仍然保持其数据有限学习的能力;（2）通过一致性和对比正则化的可扩展半监督学习，以提高模型泛化能力，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Increased connectivity of devices and people to the Internet has created an ever-expanding security attack surface. Machine learning (ML) techniques have been used to help detect attacks and may offer a more scalable way to deal with an increasingly large attack surface. However, acquiring a large volume of high-quality labelled attack samples is both costly and time consuming. Further, the acquired data set quite often do not fully represent the true data distribution. Given the challenge of labeled data scarcity and imbalance in representation, this project's novelties are to explore new ways to build data driven cyber-attack detection systems that can learn effectively from limited or biased cyber data set in a cost-efficient manner. The project's broader significance and importance are 1) enhancing the data-driven security attack detection infrastructure that leads to more secure and trustworthy cyberspace; 2) bridging the gap between research and practice by creating open-source systems that encourage real security productions, 3) providing research opportunities to both undergraduate and graduate students in the area of AI/ML enabled cyber defense.This project unveils an insight on how limited and/or imbalanced attack samples can be used as effective training data to facilitate data-driven model construction and enable high-performance security attack detection with low cost in practice. Towards this insight, this project contains three technical approaches: (1) cross-modal adversarial reprogramming that repurposes prior trained transformer models by inserting patch-level perturbations to inputs, reducing the number of parameters needed yet still maintaining its capability for data-limited learning; (2) scalable semi-supervised learning through consistency and contrastive regularization to boost model generalization for performing pseudo-labeling tasks and to help reduce label bias; (3) leveraging labeled and unlabeled objects to extend these two learning pipelines for more effective attack detection.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.