Neural Architectures for Secure Data Transmission in Next-Gen Networks

As data transmission speeds and degrees of complexity are projected to reach previously unfathomable levels soon, it is now more necessary than ever to ensure data security while it is being transported across Next-Generation Networks (NGNs). This article provides an in-depth examination of the topic “Neural Architectures for Secure Data Transmission in Next-Generation Networks.” Our research investigates the convergence of cutting-edge neural network models with the sensitive subject of network security to meet the unexpected problems provided by NGNs. The initial stages in this research are to identify and characterize the particular security concerns that NGNs confront. These problems include, for example, fast data transfer, device networking, and the proliferation of Internet of Things (IoT) devices. The proposed strategy uses the capabilities of neural architectures to improve the overall security of NGNs. This is a description of our methodology, which is divided into three sections. This algorithm class uses autoencoders to identify even little changes in network data that may signal the presence of abnormalities. As society becomes more digitally linked, the safe transmission of such data through NGNs is steadily transforming from a technological challenge to a societal imperative. This is since NGNs are being used in an increasing variety of applications. Throughout this entire effort, neural architectures are supplied as critical enabling components for the main purpose.