activity
20182022
most citedDaedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples

34 citations · 164 across the 14 of their papers we have counts for

collaborators
Showing cs.CRShow all

9 papers · 1 filter

cs.CR2022★ 2 cited

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…

cs.CR2021★ 8 cited

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…

cs.CR2020★ 7 cited

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…

cs.CR2020

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…

cs.CR2019★ 3 cited

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…

cs.CR2019★ 34 cited

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…

cs.CR2018★ 15 cited

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…

cs.CR2018★ 16 cited

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…

cs.CR2018★ 3 cited

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…