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

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,

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Institutions

Publication Details

Journal
Journal of Posthumanism
Published
2025-04-17
DOI
https://doi.org/10.63332/joph.v5i3.965
Citations
10
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
14.87
Controls
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article

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

Jobanpreet Kaur, Partha Chakraborty, Syed Hamid Hasan, Niropam Das et al.
10 citations
Journal of Posthumanism
Network Security and Intrusion Detection
14.87
article

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

Jobanpreet Kaur, Partha Chakraborty, Syed Hamid Hasan, Niropam Das, Md Salah Uddin, Rakibul Hasan, Jahid Hassan, Clinton Ronjon Barikdar, Mohammad Abdul Goffer
article en
10 citations

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,

Journal of PosthumanismVol. 5(3)
American University (US), University of California, Irvine (US)
Openalex Percentile: Top 1%
Network Security and Intrusion Detection
14.87
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