activity
20202025
most citedML Attack Models: Adversarial Attacks and Data Poisoning Attacks

31 citations · 36 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2025

Studying Various Activation Functions and Non-IID Data for Machine Learning Model Robustness

Long Dang, Thushari Hapuarachchi, Kaiqi Xiong +1

Adversarial training is an effective method to improve the machine learning (ML) model robustness. Most existing studies typically consider the Rectified linear unit (ReLU) activat…

cs.LG2024★ 2 cited

Nonlinear Transformations Against Unlearnable Datasets

Thushari Hapuarachchi, Jing Lin, Kaiqi Xiong +2

Automated scraping stands out as a common method for collecting data in deep learning models without the authorization of data owners. Recent studies have begun to tackle the priva…

cs.LG2023

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…

cs.LG2021★ 31 cited

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,…

eess.SY2021

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…

cs.LG2021★ 3 cited

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…

cs.LG2020

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…