Autonomous AI-based Cybersecurity Framework for Critical Infrastructure: Real-Time Threat Mitigation
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918262450683904 |
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| author | Paulraj, Jenifer Raghuraman, Brindha Gopalakrishnan, Nagarani Otoum, Yazan |
| author_facet | Paulraj, Jenifer Raghuraman, Brindha Gopalakrishnan, Nagarani Otoum, Yazan |
| contents | Critical infrastructure systems, including energy grids, healthcare facilities, transportation networks, and water distribution systems, are pivotal to societal stability and economic resilience. However, the increasing interconnectivity of these systems exposes them to various cyber threats, including ransomware, Denial-of-Service (DoS) attacks, and Advanced Persistent Threats (APTs). This paper examines cybersecurity vulnerabilities in critical infrastructure, highlighting the threat landscape, attack vectors, and the role of Artificial Intelligence (AI) in mitigating these risks. We propose a hybrid AI-driven cybersecurity framework to enhance real-time vulnerability detection, threat modelling, and automated remediation. This study also addresses the complexities of adversarial AI, regulatory compliance, and integration. Our findings provide actionable insights to strengthen the security and resilience of critical infrastructure systems against emerging cyber threats. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07416 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Autonomous AI-based Cybersecurity Framework for Critical Infrastructure: Real-Time Threat Mitigation Paulraj, Jenifer Raghuraman, Brindha Gopalakrishnan, Nagarani Otoum, Yazan Cryptography and Security Artificial Intelligence Emerging Technologies Machine Learning Critical infrastructure systems, including energy grids, healthcare facilities, transportation networks, and water distribution systems, are pivotal to societal stability and economic resilience. However, the increasing interconnectivity of these systems exposes them to various cyber threats, including ransomware, Denial-of-Service (DoS) attacks, and Advanced Persistent Threats (APTs). This paper examines cybersecurity vulnerabilities in critical infrastructure, highlighting the threat landscape, attack vectors, and the role of Artificial Intelligence (AI) in mitigating these risks. We propose a hybrid AI-driven cybersecurity framework to enhance real-time vulnerability detection, threat modelling, and automated remediation. This study also addresses the complexities of adversarial AI, regulatory compliance, and integration. Our findings provide actionable insights to strengthen the security and resilience of critical infrastructure systems against emerging cyber threats. |
| title | Autonomous AI-based Cybersecurity Framework for Critical Infrastructure: Real-Time Threat Mitigation |
| topic | Cryptography and Security Artificial Intelligence Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2507.07416 |