21 research outputs found
IoT-based Secure Data Transmission Prediction using Deep Learning Model in Cloud Computing
The security of Internet of Things (IoT) networks has become highly significant due to the growing number of IoT devices and the rise in data transfer across cloud networks. Here, we propose Generative Adversarial Networks (GANs) method for predicting secure data transmission in IoT-based systems using cloud computing. We evaluated our model’s attainment on the UNSW-NB15 dataset and contrasted it with other machine-learning (ML) methods, comprising decision trees (DT), random forests, and support vector machines (SVM). The outcomes demonstrate that our suggested GANs model performed better than expected in terms of precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The GANs model generates a 98.07% accuracy rate for the testing dataset with a precision score of 98.45%, a recall score of 98.19%, an F1 score of 98.32%, and an AUC-ROC value of 0.998. These outcomes show how well our suggested GANs model predicts secure data transmission in cloud-based IoT-based systems, which is a crucial step in guaranteeing the confidentiality of IoT networks
Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs
Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be
integrated into future cellular networks as new aerial mobile users. Providing
cellular connectivity to UAVs will enable a myriad of applications ranging from
online video streaming to medical delivery. However, to enable a reliable
wireless connectivity for the UAVs as well as a secure operation, various
challenges need to be addressed such as interference management, mobility
management and handover, cyber-physical attacks, and authentication. In this
paper, the goal is to expose the wireless and security challenges that arise in
the context of UAV-based delivery systems, UAV-based real-time multimedia
streaming, and UAV-enabled intelligent transportation systems. To address such
challenges, artificial neural network (ANN) based solution schemes are
introduced. The introduced approaches enable the UAVs to adaptively exploit the
wireless system resources while guaranteeing a secure operation, in real-time.
Preliminary simulation results show the benefits of the introduced solutions
for each of the aforementioned cellular-connected UAV application use case.Comment: This manuscript has been accepted for publication in IEEE Wireless
Communication