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

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 13 | 01 | Page :
    By

    Sangeeta Singh,

  1. Assistant Professor, Department of Computer Science Engineering, Madhav University, Rajasthan, India

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: Open Source Software, Artificial Intelligence, Machine Learning, Cybersecurity, Deep Learning, Intrusion Detection, OSINT

How to cite this article:
Sangeeta Singh. Open Source Software Empowering Artificial Intelligence, Machine Learning, and Cyber Security: A Comprehensive Research Study. Journal of Open Source Developments. 2026; 13(01):-.
How to cite this URL:
Sangeeta Singh. Open Source Software Empowering Artificial Intelligence, Machine Learning, and Cyber Security: A Comprehensive Research Study. Journal of Open Source Developments. 2026; 13(01):-. Available from: https://journals.stmjournals.com/joosd/article=2026/view=242380


References

1. Al-Kharusi Y, Khan A, Rizwan M, Bait-Suwailam MM. Open-source artificial intelligence
privacy and security: a review. Computers. 2024 Nov 26;13(12):311.
2. Axelsson S. Research in intrusion-detection systems: A survey. Technical report 98–17.
Department of Computer Engineering, Chalmers University of Technology; 1998 Dec 15.
3. Bishop CM, Nasrabadi NM. Pattern recognition and machine learning. New York: springer; 2006
Aug 17.
4. Buczak AL, Guven E. A survey of data mining and machine learning methods for cyber security
intrusion detection. IEEE Communications surveys & tutorials. 2015 Oct 26;18(2):1153-76.
5. Chandola V, Banerjee A, Kumar V. Anomaly detection: A survey. ACM computing surveys
(CSUR). 2009 Jul 30;41(3):1-58.
6. Jimmy F. Emerging threats: The latest cybersecurity risks and the role of artificial intelligence in
enhancing cybersecurity defenses. Valley International Journal Digital Library. 2021
Feb;1(2):564-74.
7. Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning. Cambridge: MIT press; 2016
Nov 18.
8. Kaur R, Gabrijelčič D, Klobučar T. Artificial intelligence for cybersecurity: Literature review and
future research directions. Information Fusion. 2023 Sep 1;97:101804.
9. Kim G, Lee S, Kim S. A novel hybrid intrusion detection method integrating anomaly detection
with misuse detection. Expert Systems with Applications. 2014 Mar 1;41(4):1690-700.
10. Sanz-Gómez M, Mayoral-Vilches V, Balassone F, Navarrete-Lozano LJ, Chavez CR, de Torres
MD. Cybersecurity AI Benchmark (CAIBench): A Meta-Benchmark for Evaluating
Cybersecurity AI Agents. arXiv preprint arXiv:2510.24317. 2025 Oct 28.

Ahead of Print Subscription Review Article
Volume 13
01
Received 31/03/2026
Accepted 17/04/2026
Published 30/04/2026
Publication Time 30 Days
65

Login


My IP

PlumX Metrics