Artificial Neural Network Security in Java

Abstract

Artificial Neural Network is one of the entail techniques in Artificial Intelligence for Cyber Security or policies. There are several benefits to using AI-based cyber risk analytics to improve organizational resilience and better comprehend cyber risk analytics to enhance organization. Multilayer security, convolutional networks, recurrent neural networks, self-organizing maps, deep transfer learning, as well as their ensembles and hybrid approaches, can be used to handle cycler networks efficiently. The goal of the network is to generate secure infrastructure as per input and transmit to the device as per the required output. Neural networks may be used to handle several cybersecurity problems. MLP-based networks are designed to identify the security threat of the application, provide malware protection, identify botnet traffic, and build trustworthy IOT systems. MLP is a critical feature to scale the request differently and build vulnerable IOT systems. MLP will provide the number of hidden layers, neurons, and iteration processes, solving a complex security problem and model computationally costly. This is the technique to generate security applications for infrastructure in Artificial Intelligence. It will analyze the application complexity of security and write the algorithm for different types of security, such as network security, application security, SSL security, cryptography security, and file transfer security. We don't need to spend more time designing the application for this security. It will analyze the code and write the scenario based on the application. It will generate different types of test cases also, such as functional, Integration, Code level testing,