21 citations · 22 across the 5 of their papers we have counts for
5 papers
Malformer: A Multi-Modal Malware Detector Using Transformers
Samuel Howard, Kshitiz Aryal, Mahmoud Abdelsalam +3
Traditional malware detection systems that rely on a single representation of malware often fail to identify novel threats. These representations of malware binaries, also known as…
Privacy Enhanced PEFT: Tensor Train Decomposition Improves Privacy Utility Tradeoffs under DP-SGD
Pradip Kunwar, Minh Vu, Maanak Gupta +1
Fine-tuning large language models on sensitive data poses significant privacy risks, as membership inference attacks can reveal whether individual records were used during training…
TT-LoRA MoE: Unifying Parameter-Efficient Fine-Tuning and Sparse Mixture-of-Experts
Pradip Kunwar, Minh N. Vu, Maanak Gupta +2
We propose Tensor-Trained Low-Rank Adaptation Mixture of Experts (TT-LoRA MoE), a novel computational framework integrating Parameter-Efficient Fine-Tuning (PEFT) with sparse MoE r…
SoK: Leveraging Transformers for Malware Analysis
Pradip Kunwar, Kshitiz Aryal, Maanak Gupta +2
The introduction of transformers has been an important breakthrough for AI research and application as transformers are the foundation behind Generative AI. A promising application…
A Survey on Adversarial Attacks for Malware Analysis
Kshitiz Aryal, Maanak Gupta, Mahmoud Abdelsalam
Machine learning has witnessed tremendous growth in its adoption and advancement in the last decade. The evolution of machine learning from traditional algorithms to modern deep le…