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As part of the Cyber Operative REsearch Scholars (CORES) program, a dozen students are taking part in technical and cutting-edge cybersecurity research, positioning them for cybersecurity government positions and being […]

Proposed a novel model-free label-flipping attack based on the multi-modality of the data and a novel defense technique based on the Synthetic Reduced Nearest Neighbor (SRNN) model. A preprint copy […]

SAIL Lab in collaboration with Enterprise Model Risk Management Group, Royal Bank of Canada (RBC) did research project on Deep Reinforcement Learning(DRL). The paper titled “Adversarial Attacks on Deep Algorithmic […]

Milan Zinzuvadiya’s research work under the supervision of Dr. Behzadan, titled “State-Wise Adaptive Discounting from Experience (SADE): A Novel Discounting Scheme for Reinforcement Learning” has been accepted by AAAI Conference […]

Dr. Vahid gives a presentation on security issues and challenges in Deep Reinforcement Learning in DEFCON 2020

Cytisus Eurydice gives an introduction to cyber threat intelligence investigations on the darknet, and how Ambly, the smart Darknet Spider, can help expedite the process by relieving potential bottlenecks.   […]

Dr. Behzadan’s paper with Dr. Ibrahim Baggili, titled “Founding the domain of AI Forensics” has been accepted for presentation at SafeAI 2020. A preprint copy of this paper is available […]

We hosted the first symposium of AI and Data Science Research at the University of New Haven.

The Secure and Assured Intelligent Learning (SAIL) lab is now the most recently established research group at the University of New Haven