Darknet Traffic Analysis and Network Management for Malicious Intent Detection by Neural Network Frameworks

Abstract

Security breaches may be difficult to detect because attackers are continually tweaking methods to evade detection and utilize legitimate credentials that have already been deployed in network environments. Many firms have a way to resist the evolving sophistication of attacks in network traffic analysis technology. As cloud computing, DevOps, and the internet of things (IoT) become common, it has become more difficult to maintain network visibility. Automated detection of malicious intent using a weight-agnostic neural network architecture is possible with the authors' unique darknet traffic analysis and network management technology. Intelligent forensics tool for network traffic analysis and real-time identification of encrypted information is powerful. Automated neural network search techniques based on a weight-agnostic neural network (WANNs) approach may be used to discover zero-day threats. Many firms struggle to protect their important assets because of the effort required to identify malicious intent on the darknet manually. The advanced solution proposed here overcomes such obstacles.