Open Access

ARTICLE

Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

Marya Iqbal¹, Yaser Hafeez¹, Nabil Almashfi², Amjad Alsirhani³, Faeiz Alserhani⁴, Sadia Ali¹, Mamoona Humayun^5,*, Muhammad Jamal⁶

1 University Institute of Information Technology, PMAS-Arid Agriculture University, Rawalpindi, Pakistan
2 Department of Software Engineering, College of Computer and Information Sciences, Jouf University, Al Jouf, 72388, Saudi Arabia
3 Department of Computer Science, College of Computer and Information Sciences, Jouf University, Al Jouf, 72388, Saudi Arabia
4 Department of Computer Engineering & Networks, College of Computer and Information Sciences, Jouf University, Al Jouf, 72388, Saudi Arabia
5 Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka, Al Jouf, 72388, Saudi Arabia
6 Department of Mathematics, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan

* Corresponding Author: Mamoona Humayun. Email:

(This article belongs to the Special Issue: Unveiling the Role of AIGC, Large Models, and Human - Centric Insights in Digital Defense)

Computers, Materials & Continua 2024, 79(3), 5031-5049. https://doi.org/10.32604/cmc.2024.051371

Received 04 March 2024; Accepted 06 May 2024; Issue published 20 June 2024

Abstract

Embracing software product lines (SPLs) is pivotal in the dynamic landscape of contemporary software development. However, the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability, underscoring the critical importance of robust cybersecurity measures. This paper advocates for leveraging machine learning (ML) to address variability management issues and fortify the security of SPL. In the context of the broader special issue theme on innovative cybersecurity approaches, our proposed ML-based framework offers an interdisciplinary perspective, blending insights from computing, social sciences, and business. Specifically, it employs ML for demand analysis, dynamic feature extraction, and enhanced feature selection in distributed settings, contributing to cyber-resilient ecosystems. Our experiments demonstrate the framework’s superiority, emphasizing its potential to boost productivity and security in SPLs. As digital threats evolve, this research catalyzes interdisciplinary collaborations, aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics, privacy, and human values.

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Keywords

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