103   Artículos

 
en línea
Hassan Khazane, Mohammed Ridouani, Fatima Salahdine and Naima Kaabouch    
With the rapid advancements and notable achievements across various application domains, Machine Learning (ML) has become a vital element within the Internet of Things (IoT) ecosystem. Among these use cases is IoT security, where numerous systems are dep... ver más
Revista: Future Internet    Formato: Electrónico

 
en línea
Woonghee Lee, Mingeon Ju, Yura Sim, Young Kul Jung, Tae Hyung Kim and Younghoon Kim    
Deep learning-based segmentation models have made a profound impact on medical procedures, with U-Net based computed tomography (CT) segmentation models exhibiting remarkable performance. Yet, even with these advances, these models are found to be vulner... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Meng Bi, Xianyun Yu, Zhida Jin and Jian Xu    
In this paper, we propose an Iterative Greedy-Universal Adversarial Perturbations (IGUAP) approach based on an iterative greedy algorithm to create universal adversarial perturbations for acoustic prints. A thorough, objective account of the IG-UAP metho... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
William Villegas-Ch, Angel Jaramillo-Alcázar and Sergio Luján-Mora    
This study evaluated the generation of adversarial examples and the subsequent robustness of an image classification model. The attacks were performed using the Fast Gradient Sign method, the Projected Gradient Descent method, and the Carlini and Wagner ... ver más
Revista: Big Data and Cognitive Computing    Formato: Electrónico

 
en línea
Woonghee Lee and Younghoon Kim    
This study introduces a deep-learning-based framework for detecting adversarial attacks in CT image segmentation within medical imaging. The proposed methodology includes analyzing features from various layers, particularly focusing on the first layer, a... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Sharoug Alzaidy and Hamad Binsalleeh    
In the field of behavioral detection, deep learning has been extensively utilized. For example, deep learning models have been utilized to detect and classify malware. Deep learning, however, has vulnerabilities that can be exploited with crafted inputs,... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Danilo Pau, Andrea Pisani and Antonio Candelieri    
In the context of TinyML, many research efforts have been devoted to designing forward topologies to support On-Device Learning. Reaching this target would bring numerous advantages, including reductions in latency and computational complexity, stronger ... ver más
Revista: Algorithms    Formato: Electrónico

 
en línea
Sapdo Utomo, Adarsh Rouniyar, Hsiu-Chun Hsu and Pao-Ann Hsiung    
Smart city applications that request sensitive user information necessitate a comprehensive data privacy solution. Federated learning (FL), also known as privacy by design, is a new paradigm in machine learning (ML). However, FL models are susceptible to... ver más
Revista: Future Internet    Formato: Electrónico

 
en línea
Lei Chen, Zhihao Wang, Ru Huo and Tao Huang    
As an essential piece of infrastructure supporting cyberspace security technology verification, network weapons and equipment testing, attack defense confrontation drills, and network risk assessment, Cyber Range is exceptionally vulnerable to distribute... ver más
Revista: Algorithms    Formato: Electrónico

 
en línea
Saqib Ali, Sana Ashraf, Muhammad Sohaib Yousaf, Shazia Riaz and Guojun Wang    
The successful outcomes of deep learning (DL) algorithms in diverse fields have prompted researchers to consider backdoor attacks on DL models to defend them in practical applications. Adversarial examples could deceive a safety-critical system, which co... ver más
Revista: Applied Sciences    Formato: Electrónico

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