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

About This Grant

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.