333 citations · 1.3k across the 71 of their papers we have counts for
32 papers · 1 filter
G-Fuzz: A Directed Fuzzing Framework for gVisor
Yuwei Li, Yuan Chen, Shouling Ji +6
gVisor is a Google-published application-level kernel for containers. As gVisor is lightweight and has sound isolation, it has been widely used in many IT enterprises \cite{Stripe,…
SyzTrust: State-aware Fuzzing on Trusted OS Designed for IoT Devices
Qinying Wang, Boyu Chang, Shouling Ji +8
Trusted Execution Environments (TEEs) embedded in IoT devices provide a deployable solution to secure IoT applications at the hardware level. By design, in TEEs, the Trusted Operat…
On the Security Risks of Knowledge Graph Reasoning
Zhaohan Xi, Tianyu Du, Changjiang Li +6
Knowledge graph reasoning (KGR) -- answering complex logical queries over large knowledge graphs -- represents an important artificial intelligence task, entailing a range of appli…
UVSCAN: Detecting Third-Party Component Usage Violations in IoT Firmware
Binbin Zhao, Shouling Ji, Xuhong Zhang +5
Nowadays, IoT devices integrate a wealth of third-party components (TPCs) in firmware to shorten the development cycle. TPCs usually have strict usage specifications, e.g., checkin…
Edge Deep Learning Model Protection via Neuron Authorization
Jinyin Chen, Haibin Zheng, Tao Liu +4
With the development of deep learning processors and accelerators, deep learning models have been widely deployed on edge devices as part of the Internet of Things. Edge device mod…
MINER: A Hybrid Data-Driven Approach for REST API Fuzzing
Chenyang Lyu, Jiacheng Xu, Shouling Ji +6
In recent years, REST API fuzzing has emerged to explore errors on a cloud service. Its performance highly depends on the sequence construction and request generation. However, exi…
FreeEagle: Detecting Complex Neural Trojans in Data-Free Cases
Chong Fu, Xuhong Zhang, Shouling Ji +4
Trojan attack on deep neural networks, also known as backdoor attack, is a typical threat to artificial intelligence. A trojaned neural network behaves normally with clean inputs.…
Towards Comprehensively Understanding the Run-time Security of Programmable Logic Controllers: A 3-year Empirical Study
Rongkuan Ma, Qiang Wei, Jingyi Wang +5
Programmable Logic Controllers (PLCs) are the core control devices in Industrial Control Systems (ICSs), which control and monitor the underlying physical plants such as power grid…
One Bad Apple Spoils the Barrel: Understanding the Security Risks Introduced by Third-Party Components in IoT Firmware
Binbin Zhao, Shouling Ji, Jiacheng Xu +8
Currently, the development of IoT firmware heavily depends on third-party components (TPCs) to improve development efficiency. Nevertheless, TPCs are not secure, and the vulnerabil…
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning
Pengyu Qiu, Xuhong Zhang, Shouling Ji +3
Vertical Federated Learning (VFL) is a trending collaborative machine learning model training solution. Existing industrial frameworks employ secure multi-party computation techniq…
Neural Architectural Backdoors
Ren Pang, Changjiang Li, Zhaohan Xi +2
This paper asks the intriguing question: is it possible to exploit neural architecture search (NAS) as a new attack vector to launch previously improbable attacks? Specifically, we…
An Embarrassingly Simple Backdoor Attack on Self-supervised Learning
Changjiang Li, Ren Pang, Zhaohan Xi +4
As a new paradigm in machine learning, self-supervised learning (SSL) is capable of learning high-quality representations of complex data without relying on labels. In addition to…
MPInspector: A Systematic and Automatic Approach for Evaluating the Security of IoT Messaging Protocols
Qinying Wang, Shouling Ji, Yuan Tian +8
Facilitated by messaging protocols (MP), many home devices are connected to the Internet, bringing convenience and accessibility to customers. However, most deployed MPs on IoT pla…
VeriFi: Towards Verifiable Federated Unlearning
Xiangshan Gao, Xingjun Ma, Jingyi Wang +5
Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is…
Model Inversion Attack against Transfer Learning: Inverting a Model without Accessing It
Dayong Ye, Huiqiang Chen, Shuai Zhou +3
Transfer learning is an important approach that produces pre-trained teacher models which can be used to quickly build specialized student models. However, recent research on trans…
Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era
Changjiang Li, Li Wang, Shouling Ji +4
Facial Liveness Verification (FLV) is widely used for identity authentication in many security-sensitive domains and offered as Platform-as-a-Service (PaaS) by leading cloud vendor…
Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models
Jialuo Chen, Jingyi Wang, Tinglan Peng +6
Deep learning (DL) models, especially those large-scale and high-performance ones, can be very costly to train, demanding a great amount of data and computational resources. Unauth…
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art
Xiang Ling, Lingfei Wu, Jiangyu Zhang +9
Malware has been one of the most damaging threats to computers that span across multiple operating systems and various file formats. To defend against ever-increasing and ever-evol…
Towards Robust Reasoning over Knowledge Graphs
Zhaohan Xi, Ren Pang, Changjiang Li +4
Answering complex logical queries over large-scale knowledge graphs (KGs) represents an important artificial intelligence task, entailing a range of applications. Recently, knowled…
AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy
Linkang Du, Zhikun Zhang, Shaojie Bai +4
For protecting users' private data, local differential privacy (LDP) has been leveraged to provide the privacy-preserving range query, thus supporting further statistical analysis.…
Practical Privacy Attacks on Vertical Federated Learning
Haiqin Weng, Juntao Zhang, Xingjun Ma +4
Federated learning (FL) is a privacy-preserving learning paradigm that allows multiple parities to jointly train a powerful machine learning model without sharing their private dat…
UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating Fuzzers
Yuwei Li, Shouling Ji, Yuan Chen +9
A flurry of fuzzing tools (fuzzers) have been proposed in the literature, aiming at detecting software vulnerabilities effectively and efficiently. To date, it is however still cha…
De-Health: All Your Online Health Information Are Belong to Us
Shouling Ji, Qinchen Gu, Haiqin Weng +4
In this paper, we study the privacy of online health data. We present a novel online health data De-Anonymization (DA) framework, named De-Health. De-Health consists of two phases:…
FDI: Quantifying Feature-based Data Inferability
Shouling Ji, Haiqin Weng, Yiming Wu +3
Motivated by many existing security and privacy applications, e.g., network traffic attribution, linkage attacks, private web search, and feature-based data de-anonymization, in th…