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

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