Data-driven human and bot recognition from web activity logs based on hybrid learning techniques  

作　　者：Marek Gajewski Olgierd Hryniewicz Agnieszka Jastrzębska Mariusz Kozakiewicz Karol Opara Jan Wojciech Owsiński Sławomir Zadrozny Tomasz Zwierzchowski 

机构地区：[1]Systems Research Institute,Polish Academy of Sciences,Warsaw,Poland [2]Faculty of Mathematics and Information Science,Warsaw University of Technology,Warsaw,Poland [3]EDGE NPD Ltd.Co,Warsaw,Poland

出　　处：《Digital Communications and Networks》2024年第4期1178-1188,共11页数字通信与网络（英文版）

基　　金：supported by the ABT SHIELD(Anti-Bot and Trolls Shield)project at the Systems Research Institute,Polish Academy of Sciences,in cooperation with EDGE NPD;RPMA.01.02.00-14-B448/18-00 funded by the Regional Development Fund for the development of Mazovia.

摘　　要：Distinguishing between web traffic generated by bots and humans is an important task in the evaluation of online marketing campaigns.One of the main challenges is related to only partial availability of the performance metrics:although some users can be unambiguously classified as bots,the correct label is uncertain in many cases.This calls for the use of classifiers capable of explaining their decisions.This paper demonstrates two such mechanisms based on features carefully engineered from web logs.The first is a man-made rule-based system.The second is a hierarchical model that first performs clustering and next classification using human-centred,interpretable methods.The stability of the proposed methods is analyzed and a minimal set of features that convey the classdiscriminating information is selected.The proposed data processing and analysis methodology are successfully applied to real-world data sets from online publishers.

关 键 词：Web logs Classification CLUSTERING Web traffic Bots INTERPRETABILITY 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP393.06[自动化与计算机技术—控制科学与工程]