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Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection.
Sci Rep
January 2025
North Carolina School of Science and Mathematics, Durham, NC, 27705, USA.
Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs.
View Article and Find Full Text PDFTo Attack or Not: A Neural Circuit Coding Sexually Dimorphic Aggression.
Neurosci Bull
January 2025
School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China.
An automated privacy-preserving self-supervised classification of COVID-19 from lung CT scan images minimizing the requirements of large data annotation.
Sci Rep
January 2025
Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia.
This study presents a novel privacy-preserving self-supervised (SSL) framework for COVID-19 classification from lung CT scans, utilizing federated learning (FL) enhanced with Paillier homomorphic encryption (PHE) to prevent third-party attacks during training. The FL-SSL based framework employs two publicly available lung CT scan datasets which are considered as labeled and an unlabeled dataset. The unlabeled dataset is split into three subsets which are assumed to be collected from three hospitals.
View Article and Find Full Text PDFSimplified PCNet with robustness.
Neural Netw
December 2024
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China. Electronic address:
Graph Neural Networks (GNNs) have garnered significant attention for their success in learning the representation of homophilic or heterophilic graphs. However, they cannot generalize well to real-world graphs with different levels of homophily. In response, the Poisson-Charlier Network (PCNet) (Li et al.
View Article and Find Full Text PDFAn investigation of feature reduction, transferability, and generalization in AWID datasets for secure Wi-Fi networks.
PLoS One
January 2025
Department of Computer Engineering, University of Engineering and Technology, Lahore, Pakistan.
The widespread use of wireless networks to transfer an enormous amount of sensitive information has caused a plethora of vulnerabilities and privacy issues. The management frames, particularly authentication and association frames, are vulnerable to cyberattacks and it is a significant concern. Existing research in Wi-Fi attack detection focused on obtaining high detection accuracy while neglecting modern traffic and attack scenarios such as key reinstallation or unauthorized decryption attacks.
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