42 citations · 158 across the 22 of their papers we have counts for
22 papers
Chameleon: Robust Defense Against Tor Website Fingerprinting via Many-to-Many Traffic Morphing
Yuwen Cui, Kai Wei, Kehan Shen +4
Website fingerprinting (WF) attacks can infer users' browsing activities from encrypted Tor traffic by exploiting side-channel features. Although many WF defenses have been propose…
Large Language Models and Social Media Information Integrity: Opportunities, Challenges, and Research Directions
Junjie Xiong, Zhengyuan Jiang, Xiaoran Xu +7
Large Language Models (LLMs) have emerged as powerful tools that impact information integrity on social media platforms. This comprehensive review examines the dual role of LLMs in…
When to Use Wireless Challenge-Response Physical Layer Authentication: Design of a Measurable Guideline for OFDM
Haiyun Liu, Shangqing Zhao, Yao Liu +1
The security of wireless challenge-response Physical Layer Authentication (PLA) based on Orthogonal Frequency Division Multiplexing (OFDM) relies on a sufficiently random fading ch…
Adversarial Update-Based Federated Unlearning for Poisoned Model Recovery
Wenwei Zhao, Xiaowen Li, Yao Liu +1
Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients upload manipulated updates to degrade the performance of the global model. Although detection me…
A Comprehensive Survey of Website Fingerprinting Attacks and Defenses in Tor: Advances and Open Challenges
Yuwen Cui, Guangjing Wang, Khanh Vu +7
The Tor network provides users with strong anonymity by routing their internet traffic through multiple relays. While Tor encrypts traffic and hides IP addresses, it remains vulner…
Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM
Chi Zhang, Changjia Zhu, Junjie Xiong +4
Large Language Models (LLMs) have revolutionized content creation across digital platforms, offering unprecedented capabilities in natural language generation and understanding. Me…
Detecting Adversarial Spectrum Attacks via Distance to Decision Boundary Statistics
Wenwei Zhao, Xiaowen Li, Shangqing Zhao +3
Machine learning has been adopted for efficient cooperative spectrum sensing. However, it incurs an additional security risk due to attacks leveraging adversarial machine learning…
Parrot-Trained Adversarial Examples: Pushing the Practicality of Black-Box Audio Attacks against Speaker Recognition Models
Rui Duan, Zhe Qu, Leah Ding +2
Audio adversarial examples (AEs) have posed significant security challenges to real-world speaker recognition systems. Most black-box attacks still require certain information from…
Data-Driven Next-Generation Wireless Networking: Embracing AI for Performance and Security
Jiahao Xue, Zhe Qu, Shangqing Zhao +2
New network architectures, such as the Internet of Things (IoT), 5G, and next-generation (NextG) cellular systems, put forward emerging challenges to the design of future wireless…
On the Convergence of Multi-Server Federated Learning with Overlapping Area
Zhe Qu, Xingyu Li, Jie Xu +3
Multi-server Federated learning (FL) has been considered as a promising solution to address the limited communication resource problem of single-server FL. We consider a typical mu…
Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception
Rui Duan, Zhe Qu, Shangqing Zhao +3
Recently, adversarial machine learning attacks have posed serious security threats against practical audio signal classification systems, including speech recognition, speaker reco…
Generalized Federated Learning via Sharpness Aware Minimization
Zhe Qu, Xingyu Li, Rui Duan +3
Federated Learning (FL) is a promising framework for performing privacy-preserving, distributed learning with a set of clients. However, the data distribution among clients often e…
LoMar: A Local Defense Against Poisoning Attack on Federated Learning
Xingyu Li, Zhe Qu, Shangqing Zhao +3
Federated learning (FL) provides a high efficient decentralized machine learning framework, where the training data remains distributed at remote clients in a network. Though FL en…
IoTGAN: GAN Powered Camouflage Against Machine Learning Based IoT Device Identification
Tao Hou, Tao Wang, Zhuo Lu +2
With the proliferation of IoT devices, researchers have developed a variety of IoT device identification methods with the assistance of machine learning. Nevertheless, the security…
Context-Aware Online Client Selection for Hierarchical Federated Learning
Zhe Qu, Rui Duan, Lixing Chen +3
Federated Learning (FL) has been considered as an appealing framework to tackle data privacy issues of mobile devices compared to conventional Machine Learning (ML). Using Edge Ser…
FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximation
Xingyu Li, Zhe Qu, Bo Tang +1
Federated Learning (FL) is a decentralized machine learning architecture, which leverages a large number of remote devices to learn a joint model with distributed training data. Ho…
How to Test the Randomness from the Wireless Channel for Security?
Zhe Qu, Shangqing Zhao, Jie Xu +2
We revisit the traditional framework of wireless secret key generation, where two parties leverage the wireless channel randomness to establish a secret key. The essence in the fra…
Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradients
Xingyu Li, Zhe Qu, Bo Tang +1
Federated learning (FL) is a new machine learning framework which trains a joint model across a large amount of decentralized computing devices. Existing methods, e.g., Federated A…
Cyber Deception for Computer and Network Security: Survey and Challenges
Zhuo Lu, Cliff Wang, Shangqing Zhao
Cyber deception has recently received increasing attentions as a promising mechanism for proactive cyber defense. Cyber deception strategies aim at injecting intentionally falsifie…
Adversarial Machine Learning based Partial-model Attack in IoT
Zhengping Luo, Shangqing Zhao, Zhuo Lu +2
As Internet of Things (IoT) has emerged as the next logical stage of the Internet, it has become imperative to understand the vulnerabilities of the IoT systems when supporting div…
When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions
Yalin E. Sagduyu, Yi Shi, Tugba Erpek +4
Wireless systems are vulnerable to various attacks such as jamming and eavesdropping due to the shared and broadcast nature of wireless medium. To support both attack and defense s…
When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing
Zhengping Luo, Shangqing Zhao, Zhuo Lu +2
Defense strategies have been well studied to combat Byzantine attacks that aim to disrupt cooperative spectrum sensing by sending falsified versions of spectrum sensing data to a f…