This abstract explores the paradigm of a forward-looking approach to fortifying network security in the dynamic landscape of cyber threats. The study investigates the integration of adaptive defense strategies, leveraging the capabilities of machine learning algorithms to dynamically respond to evolving cyber risks. By continuously learning from real-time data, the proposed system adapts its defense mechanisms to emerging threats, providing a proactive and resilient network security posture. The abstract emphasizes the significance of adaptability in mitigating sophisticated attacks, highlighting the effectiveness of machine learning algorithms in detecting, preventing, and responding to security incidents. Through this adaptive defense framework, organizations can foster a robust and agile security infrastructure that anticipates and counteracts cyber threats with a high degree of precision and efficiency.
Adaptive Defense: Enhancing Network Security through Machine Learning Algorithms
Publication Information
Journal Title: Asian Journal of Multidisciplinary Research & Review
Author(s): Danny Jhonson & Jane Smith
Published On: 05/03/2024
Volume: 5
Issue: 1
First Page: 134
Last Page: 145
ISSN: 2582-8088
Publisher: The Law Brigade Publisher
Cite this Article
Danny Jhonson & Jane Smith, Adaptive Defense: Enhancing Network Security through Machine Learning Algorithms, Volume 5 Issue 1, Asian Journal of Multidisciplinary Research & Review, 134-145, Published on 05/03/2024, 10.55662/AJMRR.2024.5104 Available at https://ajmrr.thelawbrigade.com/article/adaptive-defense-enhancing-network-security-through-machine-learning-algorithms/
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Copyright © 2024
Danny Jhonson & Jane Smith
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