Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

Attackers are perpetually modifying their tactics to avoid detection and frequently leverage legitimate credentials with trusted tools already deployed in a network environment, making it difficult for organizations proactively identify critical security risks. Network traffic analysis products have emerged response attackers’ relentless innovation, offering realistic path forward combatting creative attackers. Additionally, thanks the widespread adoption of cloud computing, Device Operators (DevOps) processes, Internet Things (IoT), maintaining effective visibility has become highly complex overwhelming process. What makes technology particularly meaningful is its ability combine core capabilities deliver malicious intent detection. In this paper, we propose novel darknet management framework real-time automating process, using weight agnostic neural networks architecture. It an accurate computational intelligent forensics tool analysis, demystification malware traffic, encrypted identification real time. Based on (WANNs) methodology, automated searching net architecture strategy that can perform various tasks such as identifying zero-day attacks. By process from darknet, advanced proposed solution reducing skills effort barrier prevents many effectively protecting most assets.