Federate learning on Web browsing data with statically and machine learning technique

Abstract
Purpose – Federation analytics approaches are a present area of study that has already
progressed beyond the analysis of metrics and counts. It is possible to acquire aggregated
information about on-device data by training machine learning models using federated learning
techniques without any of the raw data ever having to leave the devices in the issue. Web browser
forensics research has been focused on individual Web browsers or architectural analysis of specific
log files rather than on broad topics. This paper aims to propose major tools used for Web browser
analysis.
Design/methodology/approach – Each kind of Web browser has its own unique set of features. This
allows the user to choose their preferred browsers or to check out many browsers at once. If a forensic
examiner has access to just one Web browser’s log files, he/she makes it difficult to determine which sites a
person has visited. The agent must thus be capable of analyzing all currently available Web browsers on a
single workstation and doing an integrated study of variousWeb browsers.
Findings – Federated learning has emerged as a training paradigm in such settings. Web browser
forensics research in general has focused on certain browsers or the computational modeling of specific
log files. Internet users engage in a wide range of activities using an internet browser, such as searching
for information and sending e-mails.
Originality/value – It is also essential that the investigator have access to user activity when
conducting an inquiry. This data, which may be used to assess information retrieval activities, is
very critical. In this paper, the authors purposed a major tool used for Web browser analysis. This
study’s proposed algorithm is capable of protecting data privacy effectively in real-world
experiments.