In this study, we conduct a comprehensive review of smart grid security, exploring system
architectures, attack methodologies, defense strategies, and future research opportunities …

When mobile meets LLMs, mobile app users deserve to have more intelligent usage
experiences. For this to happen, we argue that there is a strong need to apply LLMs for the …

Recent studies show that on-device deployed deep learning (DL) models, such as those of
Tensor Flow Lite (TFLite), can be easily extracted from real-world applications and devices …

Recently, the rapid advancements of AI technologies and mobile computing have led to the
growing prevalence of smart devices and rising demands for on-device Deep Learning …

Deploying deep learning (DL) models on mobile applications (Apps) has become ever-more
popular. However, existing studies show attackers can easily reverse-engineer mobile DL …

Powered by their superior performance, deep neural networks (DNNs) have found
widespread applications across various domains. Many deep learning (DL) models are now …

In this position paper, we introduce our research objective in applying smart software
analysis for software quality assurance. We start by introducing the concepts of software …

Abstract Machine learning has been increasingly adopted across various domains due to its
outstanding performance. However, machine learning models exhibit some vulnerabilities …

Deep learning makes mobile apps smarter, but on-device DL models are vulnerable to theft.
My research shows that attackers can reverse-engineer these models to steal their details …