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Attackers are perpetually modifying their tactics to avoid detection andfrequently leverage legitimate credentials with trusted tools already deployedin a network environment, making it difficult for organizations to proactivelyidentify critical security risks. Network traffic analysis products haveemerged in response to attackers relentless innovation, offering organizationsa realistic path forward for combatting creative attackers. Additionally,thanks to the widespread adoption of cloud computing, Device Operatorsprocesses, and the Internet of Things, maintaining effective network visibilityhas become a highly complex and overwhelming process. What makes networktraffic analysis technology particularly meaningful is its ability to combineits core capabilities to deliver malicious intent detection. In this paper, wepropose a novel darknet traffic analysis and network management framework toreal-time automating the malicious intent detection process, using a weightagnostic neural networks architecture. It is an effective and accuratecomputational intelligent forensics tool for network traffic analysis, thedemystification of malware traffic, and encrypted traffic identification inreal-time. Based on Weight Agnostic Neural Networks methodology, we propose anautomated searching neural net architectures strategy that can perform varioustasks such as identify zero-day attacks. By automating the malicious intentdetection process from the darknet, the advanced proposed solution is reducingthe skills and effort barrier that prevents many organizations from effectivelyprotecting their most critical assets.

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