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DeFuzz: Deep Learning Guided Directed Fuzzing
Fuzzing is one of the most effective technique to identify potential sof...
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On the Security of Networked Control Systems in Smart Vehicle and its Adaptive Cruise Control
With the benefits of Internet of Vehicles (IoV) paradigm, come along unp...
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Analysis of Trending Topics and Text-based Channels of Information Delivery in Cybersecurity
Computer users are generally faced with difficulties in making correct s...
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Man-in-the-Middle Attacks against Machine Learning Classifiers via Malicious Generative Models
Deep Neural Networks (DNNs) are vulnerable to deliberately crafted adver...
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An Overview of Attacks and Defences on Intelligent Connected Vehicles
Cyber security is one of the most significant challenges in connected ve...
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A Feature-Oriented Corpus for Understanding, Evaluating and Improving Fuzz Testing
Fuzzing is a promising technique for detecting security vulnerabilities....
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Bug Searching in Smart Contract
With the frantic development of smart contracts on the Ethereum platform...
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Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples
We demonstrated that Non-Maximum Suppression (NMS), which is commonly us...
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Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
Machine learning based solutions have been successfully employed for aut...
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Catering to Your Concerns: Automatic Generation of Personalised Security-Centric Descriptions for Android Apps
Android users are increasingly concerned with the privacy of their data ...
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Using AI to Hack IA: A New Stealthy Spyware Against Voice Assistance Functions in Smart Phones
Intelligent Personal Assistant (IA), also known as Voice Assistant (VA),...
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Defensive Collaborative Multi-task Training - Defending against Adversarial Attack towards Deep Neural Networks
Deep neural network (DNNs) has shown impressive performance on hard perc...
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