To read this content please select one of the options below:

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

Ratnmala Nivrutti Bhimanpallewar (Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, India)
Sohail Imran Khan (Department of Business Administration, Lebanese French University, Erbil, Iraq)
K. Bhavana Raj (Department of Management Studies, Institute of Public Enterprise, Hyderabad, India)
Kamal Gulati (Amity School of Insurance, Banking and Actuarial Science, Amity University, Noida, India)
Narinder Bhasin (Amity School of Insurance, Banking and Actuarial Science, Amity University, Noida, India)
Roop Raj (Education Department, Government of Haryana, Panchkula, India)

International Journal of Pervasive Computing and Communications

ISSN: 1742-7371

Article publication date: 22 August 2022

33
This article was retracted on 7 Mar 2024.

Retraction statement

The publishers of the International Journal of Pervasive Computing and Communications wish to retract the article Bhimanpallewar, R.N., Khan, S.I., Raj, K.B., Gulati, K., Bhasin, N. and Raj, R. (2022), “Federate learning on Web browsing data with statically and machine learning technique”, International Journal of Pervasive Computing and Communications, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJPCC-05-2022-0184

An internal investigation into a series of submissions has uncovered evidence that the peer review process was compromised. As a result of these concerns, the findings of the article cannot be relied upon. This decision has been taken in accordance with Emerald's publishing ethics and the COPE guidelines on retractions. The authors of this paper would like to note that they do not agree with the content of this notice. The publishers of the journal sincerely apologize to the readers.

The retracted article is available at: 10.1108/IJPCC-05-2022-0184

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 various Web 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.

Keywords

Citation

Bhimanpallewar, R.N., Khan, S.I., Raj, K.B., Gulati, K., Bhasin, N. and Raj, R. (2022), "RETRACTED: Federate learning on Web browsing data with statically and machine learning technique", International Journal of Pervasive Computing and Communications, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJPCC-05-2022-0184

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

Related articles