39 citations · 133 across the 28 of their papers we have counts for
29 papers
PromoGuardian: Detecting Promotion Abuse Fraud with Multi-Relation Fused Graph Neural Networks
Shaofei Li, Xiao Han, Ziqi Zhang +6
As e-commerce platforms develop, fraudulent activities are increasingly emerging, posing significant threats to the security and stability of these platforms. Promotion abuse is on…
Boosting Pointer Analysis With LLM-Enhanced Allocation Function Detection
Baijun Cheng, Kailong Wang, Ling Shi +5
Pointer analysis is foundational for many static analysis tasks, yet its effectiveness is often hindered by imprecise modeling of heap allocations, particularly in C/C++ programs w…
Enhancing Semantic Understanding in Pointer Analysis using Large Language Models
Baijun Cheng, Kailong Wang, Ling Shi +4
Pointer analysis has been studied for over four decades. However, existing frameworks continue to suffer from the propagation of incorrect facts. A major limitation stems from thei…
GroupTuner: Efficient Group-Aware Compiler Auto-Tuning
Bingyu Gao, Mengyu Yao, Ziming Wang +4
Modern compilers typically provide hundreds of options to optimize program performance, but users often cannot fully leverage them due to the huge number of options. While standard…
No Data? No Problem: Synthesizing Security Graphs for Better Intrusion Detection
Yi Huang, Shaofei Li, Yao Guo +3
Provenance graph analysis plays a vital role in intrusion detection, particularly against Advanced Persistent Threats (APTs), by exposing complex attack patterns. While recent syst…
I Can Tell Your Secrets: Inferring Privacy Attributes from Mini-app Interaction History in Super-apps
Yifeng Cai, Ziqi Zhang, Mengyu Yao +8
Super-apps have emerged as comprehensive platforms integrating various mini-apps to provide diverse services. While super-apps offer convenience and enriched functionality, they ca…
Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models
Yifeng Cai, Ziqi Zhang, Ding Li +2
Modern Federated Learning (FL) has become increasingly essential for handling highly heterogeneous mobile devices. Current approaches adopt a partial model aggregation paradigm tha…
TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models
Ding Li, Ziqi Zhang, Mengyu Yao +3
Trusted Execution Environments (TEE) are used to safeguard on-device models. However, directly employing TEEs to secure the entire DNN model is challenging due to the limited compu…
SeeWasm: An Efficient and Fully-Functional Symbolic Execution Engine for WebAssembly Binaries
Ningyu He, Zhehao Zhao, Hanqin Guan +6
WebAssembly (Wasm), as a compact, fast, and isolation-guaranteed binary format, can be compiled from more than 40 high-level programming languages. However, vulnerabilities in Wasm…
Semantic-Enhanced Indirect Call Analysis with Large Language Models
Baijun Cheng, Cen Zhang, Kailong Wang +6
In contemporary software development, the widespread use of indirect calls to achieve dynamic features poses challenges in constructing precise control flow graphs (CFGs), which fu…
Query Provenance Analysis: Efficient and Robust Defense against Query-based Black-box Attacks
Shaofei Li, Ziqi Zhang, Haomin Jia +3
Query-based black-box attacks have emerged as a significant threat to machine learning systems, where adversaries can manipulate the input queries to generate adversarial examples…
SliceLocator: Locating Vulnerable Statements with Graph-based Detectors
Baijun Cheng, Kailong Wang, Cuiyun Gao +5
Vulnerability detection is a crucial component in the software development lifecycle. Existing vulnerability detectors, especially those based on deep learning (DL) models, have ac…
NODLINK: An Online System for Fine-Grained APT Attack Detection and Investigation
Shaofei Li, Feng Dong, Xusheng Xiao +6
Advanced Persistent Threats (APT) attacks have plagued modern enterprises, causing significant financial losses. To counter these attacks, researchers propose techniques that captu…
No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML
Ziqi Zhang, Chen Gong, Yifeng Cai +5
On-device ML introduces new security challenges: DNN models become white-box accessible to device users. Based on white-box information, adversaries can conduct effective model ste…
Auditing Frameworks Need Resource Isolation: A Systematic Study on the Super Producer Threat to System Auditing and Its Mitigation
Peng Jiang, Ruizhe Huang, Ding Li +5
System auditing is a crucial technique for detecting APT attacks. However, attackers may try to compromise the system auditing frameworks to conceal their malicious activities. In…
Are we there yet? An Industrial Viewpoint on Provenance-based Endpoint Detection and Response Tools
Feng Dong, Shaofei Li, Peng Jiang +8
Provenance-Based Endpoint Detection and Response (P-EDR) systems are deemed crucial for future APT defenses. Despite the fact that numerous new techniques to improve P-EDR systems…
Eunomia: Enabling User-specified Fine-Grained Search in Symbolically Executing WebAssembly Binaries
Ningyu He, Zhehao Zhao, Jikai Wang +7
Although existing techniques have proposed automated approaches to alleviate the path explosion problem of symbolic execution, users still need to optimize symbolic execution by ap…
A Survey on EOSIO Systems Security: Vulnerability, Attack, and Mitigation
Ningyu He, Haoyu Wang, Lei Wu +3
EOSIO, as one of the most representative blockchain 3.0 platforms, involves lots of new features, e.g., delegated proof of stake consensus algorithm and updatable smart contracts,…
DistFL: Distribution-aware Federated Learning for Mobile Scenarios
Bingyan Liu, Yifeng Cai, Ziqi Zhang +5
Federated learning (FL) has emerged as an effective solution to decentralized and privacy-preserving machine learning for mobile clients. While traditional FL has demonstrated its…
PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization
Bingyan Liu, Yao Guo, Xiangqun Chen
Federated learning (FL) has become a prevalent distributed machine learning paradigm with improved privacy. After learning, the resulting federated model should be further personal…
TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning
Bingyan Liu, Yifeng Cai, Yao Guo +1
The increasing of pre-trained models has significantly facilitated the performance on limited data tasks with transfer learning. However, progress on transfer learning mainly focus…
S3ML: A Secure Serving System for Machine Learning Inference
Junming Ma, Chaofan Yu, Aihui Zhou +6
We present S3ML, a secure serving system for machine learning inference in this paper. S3ML runs machine learning models in Intel SGX enclaves to protect users' privacy. S3ML desig…
Dynamic Slicing for Deep Neural Networks
Ziqi Zhang, Yuanchun Li, Yao Guo +2
Program slicing has been widely applied in a variety of software engineering tasks. However, existing program slicing techniques only deal with traditional programs that are constr…
Adversarial Attacks on Monocular Depth Estimation
Ziqi Zhang, Xinge Zhu, Yingwei Li +2
Recent advances of deep learning have brought exceptional performance on many computer vision tasks such as semantic segmentation and depth estimation. However, the vulnerability o…