AI for Multi-Modal Signal Processing in Critical IoT Infrastructure Security
Keywords:
Artificial Intelligence, Multi-Modal Signal Processing, Internet of Things, IoT SecurityAbstract
The integration of Artificial Intelligence (AI) into critical Internet of Things (IoT) infrastructure security has emerged as a vital solution to manage complex security challenges. IoT systems are inherently vulnerable to various security threats due to their massive scale and the multiplicity of data types they generate. AI techniques, particularly in multi-modal signal processing, offer promising solutions to enhance security by processing and analyzing data from various sensors in real-time. This paper explores the applications of AI for multi-modal signal processing in the context of securing critical IoT infrastructures. The research delves into key AI models and algorithms such as machine learning, deep learning, and reinforcement learning, discussing their role in detecting, mitigating, and responding to security threats. Furthermore, it highlights the challenges, advantages, and future trends in utilizing AI for ensuring robust security in IoT-based critical infrastructure.
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References
Zhang, X., & Liu, J. (2021). AI-driven anomaly detection in IoT systems. Journal of Internet of Things Security, 5(2), 105-117.
Smith, A., & Wilson, B. (2020). Machine learning for real-time security in IoT. International Journal of Cybersecurity, 7(3), 213-227.
Lee, K., & Zhang, M. (2022). Deep learning for multi-modal signal processing in IoT. Journal of AI and Machine Learning, 8(1), 42-54.
Wang, R., & Chen, T. (2021). Reinforcement learning for anomaly detection in IoT networks. Computational Intelligence Journal, 13(2), 134-145.
Hsu, Y., & Kim, C. (2020). IoT security in critical infrastructure: AI-based approaches. IEEE Transactions on Industrial Informatics, 16(3), 876-889.
Johnson, S., & Kumar, R. (2021). Predictive maintenance using AI in IoT systems. Journal of Internet and Network Systems, 12(4), 67-79.
Ali, S. A., and M. W. Zafar. "Api gateway architecture explained." INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 6.4 (2022): 54-98.
Patel, V., & Singh, A. (2022). Multi-modal data fusion for security in smart cities. Journal of Artificial Intelligence Research, 24(2), 145-157.
Zhou, Q., & Li, F. (2020). AI and machine learning for critical infrastructure protection. Journal of Cyber Defense, 11(3), 234-245.
Tan, Y., & Cheng, Z. (2021). AI-based data fusion for enhanced IoT security. Security and Privacy in IoT Systems, 5(1), 56-67.
Wang, Y., & Xie, Z. (2020). Real-time cybersecurity with machine learning in IoT networks. Journal of Security Research, 18(4), 234-246.
Huang, L., & Liu, J. (2022). Using deep learning for detecting cyber-attacks in IoT. International Journal of Cyber-Physical Systems, 15(2), 89-101.
Lee, S., & Choi, M. (2022). Blockchain and AI for enhanced IoT security. Blockchain Research and Applications, 7(1), 77-89.
Singh, H., & Gupta, P. (2021). Explainable AI in IoT security: Challenges and solutions. IEEE Access, 9, 548-563.
Zhao, R., & Jiang, T. (2022). Federated learning for IoT security: A survey. Journal of AI Security, 2(3), 109-120.
