利用变分自动编码器检测广告网络流量异常

IF 3.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2022-10-01 DOI:10.2478/jaiscr-2022-0017
Marcin Gabryel, Dawid Lada, Z. Filutowicz, Zofia Patora-Wysocka, Marek Kisiel-Dorohinicki, Guangxing Chen
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引用次数: 2

摘要

摘要:本文提出了一种神经网络模型,用于识别非人类访问网站的流量,这些流量与普通用户的访问有很大的不同。从网站所有者的角度来看,这种访问是不可取的,因为它们不是人类活动,因此不会带来任何价值,而且,更重要的是,大多数情况下涉及与处理广告相关的费用。它们通常是由不诚实的出版商使用特殊软件(机器人)来产生利润的。爬虫也被用于抓取,这是自动扫描和下载网站内容,这实际上是不符合网站作者的利益。在这项工作中提出的模型是通过在网站访问期间直接从web浏览器中提取数据来学习的。这些数据是通过使用专门准备的JavaScript获取的,该JavaScript监视用户或bot的行为。网站上出现的机器人产生的参数值与人类网站用户在典型访问期间收集的参数值有很大不同。我们不可能更多地了解控制机器人的软件,也不可能知道它们产生的所有数据。因此,本文提出了一种经过修改的变分自编码器(VAE)神经网络模型,用于检测偏离人类用户互联网流量数据的异常参数值的发生。该算法基于一种流行的自动编码器方法来检测异常,然而,一些原始的改进已经实现。在研究中,我们使用了从几家大型在线商店提取的真实数据。
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Detecting Anomalies in Advertising Web Traffic with the Use of the Variational Autoencoder
Abstract This paper presents a neural network model for identifying non-human traffic to a web-site, which is significantly different from visits made by regular users. Such visits are undesirable from the point of view of the website owner as they are not human activity, and therefore do not bring any value, and, what is more, most often involve costs incurred in connection with the handling of advertising. They are made most often by dishonest publishers using special software (bots) to generate profits. Bots are also used in scraping, which is automatic scanning and downloading of website content, which actually is not in the interest of website authors. The model proposed in this work is learnt by data extracted directly from the web browser during website visits. This data is acquired by using a specially prepared JavaScript that monitors the behavior of the user or bot. The appearance of a bot on a website generates parameter values that are significantly different from those collected during typical visits made by human website users. It is not possible to learn more about the software controlling the bots and to know all the data generated by them. Therefore, this paper proposes a variational autoencoder (VAE) neural network model with modifications to detect the occurrence of abnormal parameter values that deviate from data obtained from human users’ Internet traffic. The algorithm works on the basis of a popular autoencoder method for detecting anomalies, however, a number of original improvements have been implemented. In the study we used authentic data extracted from several large online stores.
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来源期刊
Journal of Artificial Intelligence and Soft Computing Research
Journal of Artificial Intelligence and Soft Computing Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
7.00
自引率
25.00%
发文量
10
审稿时长
24 weeks
期刊介绍: Journal of Artificial Intelligence and Soft Computing Research (available also at Sciendo (De Gruyter)) is a dynamically developing international journal focused on the latest scientific results and methods constituting traditional artificial intelligence methods and soft computing techniques. Our goal is to bring together scientists representing both approaches and various research communities.
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