2 citations · 2 across the 2 of their papers we have counts for
6 papers
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…
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…