1 code implementation • 3 Feb 2023 • Jacob Brown, Xi Jiang, Van Tran, Arjun Nitin Bhagoji, Nguyen Phong Hoang, Nick Feamster, Prateek Mittal, Vinod Yegneswaran
In this paper, we explore how machine learning (ML) models can (1) help streamline the detection process, (2) improve the potential of using large-scale datasets for censorship detection, and (3) discover new censorship instances and blocking signatures missed by existing heuristic methods.
no code implementations • 23 Apr 2021 • Prakhar Sharma, Phillip Porras, Steven Cheung, James Carpenter, Vinod Yegneswaran
We present a deep learning based approach to containerized application runtime stability analysis, and an intelligent publishing algorithm that can dynamically adjust the depth of process-level forensics published to a backend incident analysis repository.
no code implementations • 8 Jan 2019 • Anh T. Pham, Shalini Ghosh, Vinod Yegneswaran
In particular, we propose a method of masking the private data with privacy guarantee while ensuring that a classifier trained on the masked data is similar to the classifier trained on the original data, to maintain usability.
no code implementations • 16 Jul 2018 • Amir Asiaee, Hardik Goel, Shalini Ghosh, Vinod Yegneswaran, Arindam Banerjee
Stream deinterleaving is an important problem with various applications in the cybersecurity domain.
no code implementations • 18 May 2018 • Shalini Ghosh, Amaury Mercier, Dheeraj Pichapati, Susmit Jha, Vinod Yegneswaran, Patrick Lincoln
Experiments using our first approach of a multi-headed TNN model, on a dataset generated by a customized version of TORCS, show that (1) adding safety constraints to a neural network model results in increased performance and safety, and (2) the improvement increases with increasing importance of the safety constraints.