HTTP Traffic Analysis based on Multiple Deep Convolution Network Model Generation Algorithms

Bocheng Liu, Fan Yang

Abstract


In recent years, with the development of the Internet, social networking, online banking, e-commerce and other network applications are growing rapidly. At the same time, all kinds of malicious web pages are constantly emerging. Under the new situation, the network security threats are distributed, large-scale and complex. New network attack modes are emerging. With more and more diverse devices access to the Internet, our life is more intelligent and convenient, but also brings more loopholes and hidden dangers. Some malicious web pages through a variety of means to lure users to open URL links and conduct malicious behavior. However, if we can detect the URL of the malicious web page and identify the malicious web page, we can avoid the problems of content variability and behavior tracking. Therefore, traffic analysis based on various deep convolution network model generation algorithms arises at the historic moment, and becomes an important research issue in the field of Internet security.

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Keywords


URL; Traffic Analysis; Deep Learning

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Journal of Applied Data Sciences

ISSN : 2723-6471 (Online)
Organized by : Departement of Information System, Universitas Amikom Purwokerto, Indonesia; Computer Science and Systems Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia.
Website : http://bright-journal.org/JADS
Email : taqwa@amikompurwokerto.ac.id (principal contact)
    husniteja@uinjkt.ac.id (managing editor)
    support@bright-journal.org (technical issues)

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