14 citations · 23 across the 8 of their papers we have counts for
8 papers
Semantic-aware Graph-guided Behavior Sequences Generation with Large Language Models for Smart Homes
Zhiyao Xu, Dan Zhao, Qingsong Zou +4
As smart homes become increasingly prevalent, intelligent models are widely used for tasks such as anomaly detection and behavior prediction. These models are typically trained on…
FedORGP: Guiding Heterogeneous Federated Learning with Orthogonality Regularization on Global Prototypes
Fucheng Guo, Zeyu Luan, Qing Li +2
Federated Learning (FL) has emerged as an essential framework for distributed machine learning, especially with its potential for privacy-preserving data processing. However, exist…
QueryAttack: Jailbreaking Aligned Large Language Models Using Structured Non-natural Query Language
Qingsong Zou, Jingyu Xiao, Qing Li +7
Recent advances in large language models (LLMs) have demonstrated remarkable potential in the field of natural language processing. Unfortunately, LLMs face significant security an…
Synthetic User Behavior Sequence Generation with Large Language Models for Smart Homes
Zhiyao Xu, Dan Zhao, Qingsong Zou +4
In recent years, as smart home systems have become more widespread, security concerns within these environments have become a growing threat. Currently, most smart home security so…
Make Your Home Safe: Time-aware Unsupervised User Behavior Anomaly Detection in Smart Homes via Loss-guided Mask
Jingyu Xiao, Zhiyao Xu, Qingsong Zou +11
Smart homes, powered by the Internet of Things, offer great convenience but also pose security concerns due to abnormal behaviors, such as improper operations of users and potentia…
DeviceRadar: Online IoT Device Fingerprinting in ISPs using Programmable Switches
Ruoyu Li, Qing Li, Tao Lin +6
Device fingerprinting can be used by Internet Service Providers (ISPs) to identify vulnerable IoT devices for early prevention of threats. However, due to the wide deployment of mi…
Genos: General In-Network Unsupervised Intrusion Detection by Rule Extraction
Ruoyu Li, Qing Li, Yu Zhang +3
Anomaly-based network intrusion detection systems (A-NIDS) use unsupervised models to detect unforeseen attacks. However, existing A-NIDS solutions suffer from low throughput, lack…
Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models
Zhengxin Zhang, Dan Zhao, Xupeng Miao +4
Finetuning large language models (LLMs) has been empirically effective on a variety of downstream tasks. Existing approaches to finetuning an LLM either focus on parameter-efficien…