18 citations · 49 across the 11 of their papers we have counts for
11 papers
Risks of Practicing Large Language Models in Smart Grid: Threat Modeling and Validation
Jiangnan Li, Yingyuan Yang, Jinyuan Sun
Large language models (LLMs) represent significant breakthroughs in artificial intelligence and hold potential for applications within smart grids. However, as demonstrated in prev…
Mitigating Adversarial Effects of False Data Injection Attacks in Power Grid
Farhin Farhad Riya, Shahinul Hoque, Yingyuan Yang +3
Deep Neural Networks have proven to be highly accurate at a variety of tasks in recent years. The benefits of Deep Neural Networks have also been embraced in power grids to detect…
Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids
Jiangnan Li, Yingyuan Yang, Jinyuan Stella Sun +2
False data injection attacks (FDIAs) pose a significant security threat to power system state estimation. To detect such attacks, recent studies have proposed machine learning (ML)…
Exploiting Vulnerabilities of Deep Learning-based Energy Theft Detection in AMI through Adversarial Attacks
Jiangnan Li, Yingyuan Yang, Jinyuan Stella Sun
Effective detection of energy theft can prevent revenue losses of utility companies and is also important for smart grid security. In recent years, enabled by the massive fine-grai…
BubbleMap: Privilege Mapping for Behavior-based Implicit Authentication Systems
Yingyuan Yang, Xueli Huang, Jiangnan Li +1
Leveraging users' behavioral data sampled by various sensors during the identification process, implicit authentication (IA) relieves users from explicit actions such as rememberin…
EchoIA: Implicit Authentication System Based on User Feedback
Yingyuan Yang, Xueli Huang, Jiangnan Li +1
Implicit authentication (IA) transparently authenticates users by utilizing their behavioral data sampled from various sensors. Identifying the illegitimate user through constantly…
SearchFromFree: Adversarial Measurements for Machine Learning-based Energy Theft Detection
Jiangnan Li, Yingyuan Yang, Jinyuan Stella Sun
Energy theft causes large economic losses to utility companies around the world. In recent years, energy theft detection approaches based on machine learning (ML) techniques, espec…
ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems
Jiangnan Li, Yingyuan Yang, Jinyuan Stella Sun +2
Recent research demonstrated that the superficially well-trained machine learning (ML) models are highly vulnerable to adversarial examples. As ML techniques are becoming a popular…
SmartBullets: A Cloud-Assisted Bullet Screen Filter based on Deep Learning
Haoran Niu, Jiangnan Li, Yu Zhao
Bullet-screen is a technique that enables the website users to send real-time comment `bullet' cross the screen. Compared with the traditional review of a video, bullet-screen prov…
Dynamic Detection of False Data Injection Attack in Smart Grid using Deep Learning
Xiangyu Niu Jiangnan Li, Jinyuan Sun
Modern advances in sensor, computing, and communication technologies enable various smart grid applications. The heavy dependence on communication technology has highlighted the vu…
A Practical Searchable Symmetric Encryption Scheme for Smart Grid Data
Jiangnan Li, Xiangyu Niu, Jinyuan Stella Sun
Outsourcing data storage to the remote cloud can be an economical solution to enhance data management in the smart grid ecosystem. To protect the privacy of data, the utility compa…