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

arXiv: https://arxiv.org/abs/2102.08411 · HTML: https://mlsanity.com/paper/2102.08411/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 89 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Auto-Keras: An Efficient Neural Architecture Search System](https://mlsanity.com/paper/1806.10282/) (2018)
- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [Meta-learning within Projective Simulation](https://mlsanity.com/paper/1602.08017/) (2016)
- [Shapley Homology: Topological Analysis of Sample Influence for Neural Networks](https://mlsanity.com/paper/1910.06509/) (2019)
- [Finding Cryptocurrency Attack Indicators Using Temporal Logic and Darkweb Data](https://mlsanity.com/paper/1810.12906/) (2018)

## Built on by (most cited first, 1 of 1)

- [Darknet Traffic Classification and Adversarial Attacks](https://mlsanity.com/paper/2206.06371/) (2022)

## Abstract (arXiv)

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.