133 citations · 224 across the 19 of their papers we have counts for
21 papers
Diagonalization of skew-Hermitian dual quaternion matrices and -Hermitian dual quaternion matrices
Cailing Yao, Bingzhe Hou
In this article, we present the diagonalization of skew-Hermitian dual quaternion matrices in the sense of unitary equivalence. For a unit quaternion , we also obtain the diagon…
PUFFERDOS: Efficient and Effective Attack String Generation for Regular Expression Denial of Service Vulnerabilities
Shangzhi Xu, Ziqi Ding, Xiao Cheng +5
ReDoS attacks constitute a critical class of resource-exhaustion vulnerabilities. In such attacks, adversaries exploit the pathological worst-case execution behavior of regular exp…
Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy
Zhichao You, Xuewen Dong, Shujun Li +3
Reconstruction attacks against federated learning (FL) aim to reconstruct users' samples through users' uploaded gradients. Local differential privacy (LDP) is regarded as an effec…
Automated TEE Adaptation with LLMs: Identifying, Transforming, and Porting Sensitive Functions in Programs
Ruidong Han, Zhou Yang, Chengyan Ma +5
Trusted Execution Environments (TEEs) isolate a special space within a device memory that is not accessible to the normal world (also known as the untrusted environment), even when…
Enhancing Security in Third-Party Library Reuse -- Comprehensive Detection of 1-day Vulnerability through Code Patch Analysis
Shangzhi Xu, Jialiang Dong, Weiting Cai +4
Nowadays, software development progresses rapidly to incorporate new features. To facilitate such growth and provide convenience for developers when creating and updating software,…
Reducing Adversarial Training Cost with Gradient Approximation
Huihui Gong
Deep learning models have achieved state-of-the-art performances in various domains, while they are vulnerable to the inputs with well-crafted but small perturbations, which are na…
Parameter-Saving Adversarial Training: Reinforcing Multi-Perturbation Robustness via Hypernetworks
Huihui Gong, Minjing Dong, Siqi Ma +3
Adversarial training serves as one of the most popular and effective methods to defend against adversarial perturbations. However, most defense mechanisms only consider a single ty…
Stealthy Physical Masked Face Recognition Attack via Adversarial Style Optimization
Huihui Gong, Minjing Dong, Siqi Ma +3
Deep neural networks (DNNs) have achieved state-of-the-art performance on face recognition (FR) tasks in the last decade. In real scenarios, the deployment of DNNs requires taking…
SemDiff: Binary Similarity Detection by Diffing Key-Semantics Graphs
Zian Liu, Zhi Zhang, Siqi Ma +6
Binary similarity detection is a critical technique that has been applied in many real-world scenarios where source code is not available, e.g., bug search, malware analysis, and c…
Two-in-one Knowledge Distillation for Efficient Facial Forgery Detection
Chuyang Zhou, Jiajun Huang, Daochang Liu +4
Facial forgery detection is a crucial but extremely challenging topic, with the fast development of forgery techniques making the synthetic artefact highly indistinguishable. Prior…
Anti-Compression Contrastive Facial Forgery Detection
Jiajun Huang, Xinqi Zhu, Chengbin Du +3
Forgery facial images and videos have increased the concern of digital security. It leads to the significant development of detecting forgery data recently. However, the data, espe…
EvilScreen Attack: Smart TV Hijacking via Multi-channel Remote Control Mimicry
Yiwei Zhang, Siqi Ma, Tiancheng Chen +3
Modern smart TVs often communicate with their remote controls (including those smart phone simulated ones) using multiple wireless channels (e.g., Infrared, Bluetooth, and Wi-Fi).…
Control Parameters Considered Harmful: Detecting Range Specification Bugs in Drone Configuration Modules via Learning-Guided Search
Ruidong Han, Chao Yang, Siqi Ma +4
In order to support a variety of missions and deal with different flight environments, drone control programs typically provide configurable control parameters. However, such a fle…
Dep: Mutation-based Dependency Generation for Precise Taint Analysis on Android Native Code
Cong Sun, Yuwan Ma, Dongrui Zeng +3
The existence of native code in Android apps plays an important role in triggering inconspicuous propagation of secrets and circumventing malware detection. However, the state-of-t…
CryptoEval: Evaluating the Risk of Cryptographic Misuses in Android Apps with Data-Flow Analysis
Cong Sun, Xinpeng Xu, Yafei Wu +4
The misunderstanding and incorrect configurations of cryptographic primitives have exposed severe security vulnerabilities to attackers. Due to the pervasiveness and diversity of c…
Sdft: A PDG-based Summarization for Efficient Dynamic Data Flow Tracking
Xiao Kan, Cong Sun, Shen Liu +4
Dynamic taint analysis (DTA) has been widely used in various security-relevant scenarios that need to track the runtime information flow of programs. Dynamic binary instrumentation…
ReCFA: Resilient Control-Flow Attestation
Yumei Zhang, Xinzhi Liu, Cong Sun +4
Recent IoT applications gradually adapt more complicated end systems with commodity software. Ensuring the runtime integrity of these software is a challenging task for the remote…
Fine with "1234"? An Analysis of SMS One-Time Password Randomness in Android Apps
Siqi Ma, Juanru Li, Hyoungshick Kim +4
A fundamental premise of SMS One-Time Password (OTP) is that the used pseudo-random numbers (PRNs) are uniquely unpredictable for each login session. Hence, the process of generati…
Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review
Yansong Gao, Bao Gia Doan, Zhi Zhang +5
This work provides the community with a timely comprehensive review of backdoor attacks and countermeasures on deep learning. According to the attacker's capability and affected st…
Revocable Federated Learning: A Benchmark of Federated Forest
Yang Liu, Zhuo Ma, Ximeng Liu +3
A learning federation is composed of multiple participants who use the federated learning technique to collaboratively train a machine learning model without directly revealing the…
Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing
Yang Liu, Zhuo Ma, Ximeng Liu +3
Recently, Google and other 24 institutions proposed a series of open challenges towards federated learning (FL), which include application expansion and homomorphic encryption (HE)…