Dark-Forest: Analysis on the Behavior of Dark Web Traffic via DeepForest and PSO Algorithm-Reference-Cited by-同舟云学术

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Dark-Forest: Analysis on the Behavior of Dark Web Traffic via DeepForest and PSO Algorithm

Published:2023 Issue:1 Volume:135 Page:561-581
ISSN:1526-1506
Container-title:Computer Modeling in Engineering & Sciences
language:en
Short-container-title:

Author:

Tong Xin,Zhang Changlin,Wang Jingya,Zhao Zhiyan,Liu Zhuoxian

Publisher

Computers, Materials and Continua (Tech Science Press)

Subject

Computer Science Applications,Modeling and Simulation,Software

Reference33 articles.

1. Why the ARPANET was built;Lukasik;IEEE Annals of the History of Computing,2010

2. Digital 2022: Another year of bumper growth (2022). https://wearesocial.com/us/blog/2022/01/digital-2022-another-year-of-bumper-growth-2/.

3. Deep forest;Zhou;National Science Review,2019

4. Particle swarm optimization;Kennedy,1995

5. DIDarknet: A contemporary approach to detect and characterize the darknet traffic using deep image learning;Habibi,2020

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Darknet Traffic Analysis: A Systematic Literature Review;IEEE Access;2024

2. A network traffic identification method based on AutoEncoder - a feature selection algorithm;Journal of Physics: Conference Series;2023-09-01

3. AE-DTI: An Efficient Darknet Traffic Identification Method Based on Autoencoder Improvement;Applied Sciences;2023-08-17

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