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All Journal Journal of Applied Data Sciences
Bocheng Liu
School of Software, Nanchang University, China
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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HTTP Traffic Analysis based on Multiple Deep Convolution Network Model Generation Algorithms Bocheng Liu; Fan Yang
Journal of Applied Data Sciences Vol 3, No 4: DECEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i4.69

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.
Co-Authors Fan Yang