34 citations · 38 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…