Adaptive Cyber Defense Strategies Using Machine Learning to Counter Advanced Persistent Threats

Akash Arun Kumar Soumya

Abstract: The study effectively examines the role of machine learning in enhancing cybersecurity defenses against advanced persistent threats (APTs). In recent times, cyber threats have become more advanced and persistent. In this regard, traditional security measures have proved inadequate. Machine learning offers an effective solution by improving threat detection accuracy and response time. The application of both unsupervised and supervised learning techniques enables organizations to identify both known and unknown threats effectively. The recent advancements in deep learning effectively improved machine learning's overall capabilities and allowed for the analysis and complex data patterns which could indicate APTs. While the incorporation of machine learning presents challenges such as data privacy and model interpretability, it significantly strengthens adaptive cyber defense strategies.

Keywords: Advanced Persistent Threats (APTs), Machine Learning (ML), Cybersecurity, Deep Learning, Intrusion Detection

How to Cite?: Akash Arun Kumar Soumya, "Adaptive Cyber Defense Strategies Using Machine Learning to Counter Advanced Persistent Threats", Volume 14 Issue 12, December 2025, International Journal of Science and Research (IJSR), Pages: 2371-2373, https://www.ijsr.net/getabstract.php?paperid=SR251228203105, DOI: https://dx.doi.org/10.21275/SR251228203105