Intelligent Cybersecurity Systems to Safeguard U.S. National Interests Using AI and Machine Learning

Authors

  • Jawad Sarwar School of IT, Washington University of Science and Technology
  • Vivek Kumar Department of IT, Cloudy Data
  • Sadiya Afrin School of IT, Washington University of Science and Technology
  • Amit Banwari Gupta School of IT, Washington University of Science and Technology

DOI:

https://doi.org/10.71437/rjems.v1i2.35

Keywords:

Artificial Intelligence, Machine Learning, Cybersecurity, National Security, Critical Infrastructure Protection

Abstract

The rising tide of cyber threats in frequency, scope and sophistication, has been of great concern to the national interests of the United States, especially in the areas of critical infrastructure, defense systems and government networks. Traditional rulebased and signature-driven cybersecurity solutions are less and less sufficient to deal with advanced and adaptive attacks such as zero-day exploits and advanced persistent threats. This challenge has spurred the rapid development of interest in smart cybersecurity systems that use artificial intelligence (AI) and machine learning (ML) to bolster threat detection, prediction and response capabilities.
Despite the increasing use of AI-enabled security tools, there is a research gap that remains in the synthesis of conceptual architectures with analytical insights to directly relate AI-enabled cybersecurity mechanisms to national security objectives. Existing studies tend to concentrate on discrete technical models without adequately addressing the system level of design, operational scalability, and policy relevance in the context of U.S. national defense and critical infrastructure protection.
This study takes a combined conceptual and analytical approach to study intelligent cybersecurity systems that are designed to protect U.S. national interests. A layered architecture of AI-based cybersecurity is proposed, which utilizes supervised learning, unsupervised learning, and deep learning for intrusion detection, finding an anomaly, and adapting to the threat.
Comparative analytical evaluation is used for assessing the performance and strategic applicability of models for national
security domains.
The findings point out that AI- and ML-powered systems have a major influence on being much better in detection accuracy, response time and adaptability than conventional cybersecurity frameworks. The study makes a structured contribution, connecting technical capabilities and strategic national interests and includes the identification of major issues concerning ethics, governance and system resilience. These insights provide useful information for policy makers and security practitioners trying to bolster the national cyber defense with smart, scaleable, and policy-aligned cybersecurity solutions.

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Published

2025-09-28

How to Cite

Sarwar, J., Kumar , V., Afrin, S., & Gupta, A. B. (2025). Intelligent Cybersecurity Systems to Safeguard U.S. National Interests Using AI and Machine Learning. Research Journal of Engineering and Medical Science, 1(2), 1–13. https://doi.org/10.71437/rjems.v1i2.35