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Mehdi Sadi, Bashir Mohammad Sabquat Bahar Talukder, Kaniz Mishty and Md Tauhidur Rahman
Universal adversarial perturbations are image-agnostic and model-independent noise that, when added to any image, can mislead the trained deep convolutional neural networks into the wrong prediction. Since these universal adversarial perturbations can se...
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Sapna Sadhwani, Baranidharan Manibalan, Raja Muthalagu and Pranav Pawar
The study in this paper characterizes lightweight IoT networks as being established by devices with few computer resources, such as reduced battery life, processing power, memory, and, more critically, minimal security and protection, which are easily vu...
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Angelo Cannarile, Vincenzo Dentamaro, Stefano Galantucci, Andrea Iannacone, Donato Impedovo and Giuseppe Pirlo
Recognition of malware is critical in cybersecurity as it allows for avoiding execution and the downloading of malware. One of the possible approaches is to analyze the executable?s Application Programming Interface (API) calls, which can be done using t...
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Eungyu Lee, Yongsoo Lee and Teajin Lee
Many studies attempt to apply artificial intelligence (AI) to cyber security to effectively cope with the increasing number of cyber threats. However, there is a black box problem such that it is difficult to understand the basis for AI prediction. False...
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Arthur Fournier, Franjieh El Khoury and Samuel Pierre
The rapid adoption of Android devices comes with the growing prevalence of mobile malware, which leads to serious threats to mobile phone security and attacks private information on mobile devices. In this paper, we designed and implemented a model for m...
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