34 citations · 164 across the 14 of their papers we have counts for
14 papers
The "Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices
Wanlun Ma, Derui Wang, Ruoxi Sun +3
Deep Neural Networks (DNNs) are susceptible to backdoor attacks during training. The model corrupted in this way functions normally, but when triggered by certain patterns in the i…
Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques
Huacheng Li, Chunhe Xia, Tianbo Wang +3
Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subject…
Snipuzz: Black-box Fuzzing of IoT Firmware via Message Snippet Inference
Xiaotao Feng, Ruoxi Sun, Xiaogang Zhu +5
The proliferation of Internet of Things (IoT) devices has made people's lives more convenient, but it has also raised many security concerns. Due to the difficulty of obtaining and…
DeFuzz: Deep Learning Guided Directed Fuzzing
Xiaogang Zhu, Shigang Liu, Xian Li +4
Fuzzing is one of the most effective technique to identify potential software vulnerabilities. Most of the fuzzers aim to improve the code coverage, and there is lack of directedne…
Analysis of Trending Topics and Text-based Channels of Information Delivery in Cybersecurity
Tingmin Wu, Wanlun Ma, Sheng Wen +4
Computer users are generally faced with difficulties in making correct security decisions. While an increasingly fewer number of people are trying or willing to take formal securit…
Man-in-the-Middle Attacks against Machine Learning Classifiers via Malicious Generative Models
Derui, Wang, Chaoran Li +3
Deep Neural Networks (DNNs) are vulnerable to deliberately crafted adversarial examples. In the past few years, many efforts have been spent on exploring query-optimisation attacks…
An Overview of Attacks and Defences on Intelligent Connected Vehicles
Mahdi Dibaei, Xi Zheng, Kun Jiang +9
Cyber security is one of the most significant challenges in connected vehicular systems and connected vehicles are prone to different cybersecurity attacks that endanger passengers…
A Feature-Oriented Corpus for Understanding, Evaluating and Improving Fuzz Testing
Xiaogang Zhu, Xiaotao Feng, Tengyun Jiao +4
Fuzzing is a promising technique for detecting security vulnerabilities. Newly developed fuzzers are typically evaluated in terms of the number of bugs found on vulnerable programs…
Bug Searching in Smart Contract
Xiaotao Feng, Qin Wang, Xiaogang Zhu +1
With the frantic development of smart contracts on the Ethereum platform, its market value has also climbed. In 2016, people were shocked by the loss of nearly $50 million in crypt…
Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples
Derui Wang, Chaoran Li, Sheng Wen +4
This paper demonstrates that Non-Maximum Suppression (NMS), which is commonly used in Object Detection (OD) tasks to filter redundant detection results, is no longer secure. Consid…
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
Xiao Chen, Chaoran Li, Derui Wang +5
Machine learning based solutions have been successfully employed for automatic detection of malware on Android. However, machine learning models lack robustness to adversarial exam…
Using AI to Hack IA: A New Stealthy Spyware Against Voice Assistance Functions in Smart Phones
Rongjunchen Zhang, Xiao Chen, Jianchao Lu +3
Intelligent Personal Assistant (IA), also known as Voice Assistant (VA), has become increasingly popular as a human-computer interaction mechanism. Most smartphones have built-in v…
Catering to Your Concerns: Automatic Generation of Personalised Security-Centric Descriptions for Android Apps
Tingmin Wu, Lihong Tang, Rongjunchen Zhang +5
Android users are increasingly concerned with the privacy of their data and security of their devices. To improve the security awareness of users, recent automatic techniques produ…
Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training
Derek Wang, Chaoran Li, Sheng Wen +2
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples which contain human-imperceptible perturbations. A series of defending methods, either proactive defe…