ICETETI-2024 | View PDF
Paper Id:ICETETI-MSM104
doi: 10.71141/30485037/ICETETI-MSM104

AI-Driven Threat Intelligence for Securing Embedded Systems in Critical Infrastructure

Maria Lopez, Karthikeyan Muthusamy

Citation:
Maria Lopez, Karthikeyan Muthusamy, "AI-Driven Threat Intelligence for Securing Embedded Systems in Critical Infrastructure" International Journal of Multidisciplinary on Science and Management, pp. 28-36, 2024.

Abstract:
As embedded systems become integral to critical infrastructure, they increasingly face sophisticated cyber threats. Traditional security measures often fall short against rapidly evolving attack vectors, necessitating advanced solutions. Artificial Intelligence (AI)-driven threat intelligence offers a proactive approach by leveraging machine learning, deep learning, and data analytics to detect, analyze, and mitigate cyber threats in real time. This paper explores the application of AI-driven threat intelligence in securing embedded systems within critical infrastructure, emphasizing anomaly detection, behavioral analysis, and automated response mechanisms. We also discuss challenges such as adversarial AI, data privacy concerns, and computational constraints, proposing future directions for enhancing AI-based security frameworks. Our findings suggest that AI-driven threat intelligence significantly improves the resilience of embedded systems, making them more robust against cyber-attacks in mission-critical applications.

Keywords: AI-Driven Threat Intelligence, Embedded Systems Security, Critical Infrastructure, Anomaly Detection, Adversarial AI, Cybersecurity.

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