31 citations · 58 across the 5 of their papers we have counts for
6 papers
Improving Machine Learning Robustness via Adversarial Training
Long Dang, Thushari Hapuarachchi, Kaiqi Xiong +1
As Machine Learning (ML) is increasingly used in solving various tasks in real-world applications, it is crucial to ensure that ML algorithms are robust to any potential worst-case…
ML Attack Models: Adversarial Attacks and Data Poisoning Attacks
Jing Lin, Long Dang, Mohamed Rahouti +1
Many state-of-the-art ML models have outperformed humans in various tasks such as image classification. With such outstanding performance, ML models are widely used today. However,…
Mahalanobis distance-based robust approaches against false data injection attacks on dynamic power state estimation
Jing Lin, Kaiqi Xiong
Many researchers have studied false data injection (FDI) attacks in power state estimation, but existing state estimation approaches are still highly vulnerable to FDI attacks. In…
Active Learning Under Malicious Mislabeling and Poisoning Attacks
Jing Lin, Ryan Luley, Kaiqi Xiong
Deep neural networks usually require large labeled datasets for training to achieve state-of-the-art performance in many tasks, such as image classification and natural language pr…
An Adversarial Attack Defending System for Securing In-Vehicle Networks
Yi Li, Jing Lin, Kaiqi Xiong
In a modern vehicle, there are over seventy Electronics Control Units (ECUs). For an in-vehicle network, ECUs communicate with each other by following a standard communication prot…
A Survey on Security Attacks and Defense Techniques for Connected and Autonomous Vehicles
Minh Pham, Kaiqi Xiong
Autonomous Vehicle has been transforming intelligent transportation systems. As telecommunication technology improves, autonomous vehicles are getting connected to each other and t…