AI-Enhanced Cyber Threat Detection and Response Advancing National Security in Critical Infrastructure

Authors

  • Mohammad Abdul Goffer School of Business, International American University, Los Angeles, CA 90010, USA
  • Md Salah Uddin College of Technology & Engineering, Westcliff University, CA 92614, USA
  • Jobanpreet kaur College of Technology & Engineering, Westcliff University, CA 92614, USA
  • Syed Nazmul Hasan College of Technology & Engineering, Westcliff University, CA 92614, USA
  • Clinton Ronjon Barikdar School of Business, International American University, Los Angeles, CA 90010, USA
  • Jahid Hassan School of Business, International American University, Los Angeles, CA 90010, USA
  • Niropam Das School of Business, International American University, Los Angeles, CA 90010, USA
  • Partha Chakraborty School of Business, International American University, Los Angeles, CA 90010, USA
  • Rakibul Hasan Department of Business Administration, Westcliff University, Irvine, CA 92614, USA

DOI:

https://doi.org/10.63332/joph.v5i3.965

Keywords:

Artificial Intelligence, Cyber Threat Detection, Critical Infrastructure, National Security, Intrusion Detection Systems, AI-Driven Security

Abstract

Rapid digitalization of essential national infrastructure has created new vulnerabilities to cyber threats, leading to major security threats against the nation. The current security measures prove inadequate for keeping pace with developing cyberattacks, so artificial intelligence needs integration for threat detection enhancements and response improvements. The combination of AI-enabled cybersecurity systems gives them the ability to examine huge data collections instantly, monitor irregularities and perform automatic threat response functions to improve national security. Research investigates the system of artificial intelligence to enhance cyber threat response capabilities alongside its specific use for defending crucial infrastructure elements like energy networks as well as financial organizations and government IT infrastructure. Methodology The study combines qualitative approaches with quantitative methods as its research methodology. The analysis includes a structured review of existing frameworks that use AI for cybersecurity purposes  with their performance evaluation. The paper evaluates real-world AI deployments across critical infrastructure systems through case studies to reveal successful strategies with encountered problems. The empirical proof of machine learning-based intrusion detection systems is carried out by testing IDS along with real-world dataset assessment to verify AI's threat mitigation effectiveness through the accuracy and precision & recall method. Security experts who perform interviews deliver valuable information about the current use of AI technology in national security applications. The national cybersecurity capabilities gain strength from AI-driven systems because these systems accomplish improved threat detection and swift responses without requiring human involvement. AI deliver its maximum effectiveness only when data privacy issues with adversarial AI attacks and regulatory hurdles, receive proper solutions,

Downloads

Published

2025-04-17

How to Cite

Goffer, M. A., Uddin, M. S., kaur, J., Hasan, S. N., Barikdar, C. R., Hassan, J., … Hasan, R. (2025). AI-Enhanced Cyber Threat Detection and Response Advancing National Security in Critical Infrastructure . Journal of Posthumanism, 5(3), 1667–1689. https://doi.org/10.63332/joph.v5i3.965

Issue

Section

Articles