5 citations · 6 across the 5 of their papers we have counts for
6 papers
MACGen: Toward Functionally Correct and Secure Code Generation via Multi-Agent Collaboration
Miseon Yu, Jaehoon Choi, Younghan Lee +1
Despite their strong ability to generate code, large language models often fail to produce secure code, as their outputs frequently contain security vulnerabilities. Secure code ge…
Rethinking the Capability of Fine-Tuned Language Models for Automated Vulnerability Repair
Woorim Han, Yeongjun Kwak, Miseon Yu +4
Learning-based automated vulnerability repair (AVR) techniques that utilize fine-tuned language models have shown promise in generating vulnerability patches. However, questions re…
VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification
Yungi Cho, Woorim Han, Miseon Yu +3
Vertical Federated Learning (VFL) focuses on handling vertically partitioned data over FL participants. Recent studies have discovered a significant vulnerability in VFL to backdoo…
Precise Extraction of Deep Learning Models via Side-Channel Attacks on Edge/Endpoint Devices
Younghan Lee, Sohee Jun, Yungi Cho +3
With growing popularity, deep learning (DL) models are becoming larger-scale, and only the companies with vast training datasets and immense computing power can manage their busine…
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models
Younghan Lee, Yungi Cho, Woorim Han +2
Federated Learning (FL) thrives in training a global model with numerous clients by only sharing the parameters of their local models trained with their private training datasets.…
Extended Abstract: Mimicry Resilient Program Behavior Modeling with LSTM based Branch Models
Hayoon Yi, Gyuwan Kim, Jangho Lee +4
In the software design, protecting a computer system from a plethora of software attacks or malware in the wild has been increasingly important. One branch of research to detect th…