STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Open Source Developments Review Article

Open Source Software Empowering Artificial Intelligence, Machine Learning, and Cyber Security: A Comprehensive Research Study

  1. Sangeeta Singh Department of Computer Science Engineering, Madhav University

Abstract

Open Source Software (OSS) has become a foundational pillar for rapid innovation across Artificial Intelligence (AI), Machine Learning (ML), and Cybersecurity. This paper delivers a comprehensive, journal-length analysis of OSS-driven ecosystems, emphasizing collaborative development, transparency, and accelerated deployment. By providing freely available libraries, tools, and frameworks, OSS makes it easier for developers and researchers to experiment, build models, and deploy solutions quickly. This study examines how OSS can be combined with AI and ML to improve cybersecurity, using a layered framework that covers data collection, processing, model training, threat detection, and automated response. Popular OSS tools are reviewed, showing how they improve detection accuracy, system efficiency, and scalability. While OSS offers many advantages, it also presents challenges such as exposed vulnerabilities, inconsistent updates, limited documentation, and integration difficulties. Strategies like secure coding, community oversight, and privacy-focused methods such as federated learning can help overcome these issues. The paper also discusses future directions, including explainable AI, autonomous security systems, and blockchain-based protection. Overall, OSS combined with AI and ML provides transparent, collaborative, and adaptable solutions that strengthen cybersecurity and support faster innovation across industries.

Keywords

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