Proactive Security for the Industrial IoT Networks in the AI Era: A Framework for Continuous Threat Exposure Management
DOI:
https://doi.org/10.32628/CSEIT251112162Keywords:
AI Security Integration, Continuous Threat Exposure Management, Industrial IoT Security, Proactive Security Framework, Real-time Threat DetectionAbstract
This article investigates the convergence of Industrial Internet of Things (IIoT) technologies and Artificial Intelligence (AI), exploring the evolving landscape of AI-powered attacks and the critical need for proactive security measures. To mitigate these risks, this paper proposes a multi-faceted security approach that combines traditional security techniques with AI-driven solutions. Focusing on Continuous Threat Exposure Management (CTEM) and Attack Surface Management (ASM), and emphasizing real-time threat detection, automated response mechanisms, and a robust human-AI collaboration model, the study explores a paradigm shift from reactive to proactive security measures in response to the evolving cyber threats facing modern industrial environments. Through an in-depth analysis of successful security implementations across diverse industrial sectors, this research demonstrates the efficacy of integrating AI-driven security measures with existing infrastructure. By proactively addressing these challenges, organizations can harness the power of AI to safeguard their digital assets and ensure the integrity of their operations.
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References
Grand View Research, "Artificial Intelligence Market Size, Share & Trends Analysis Report," National Quality Registry (NQR), Technical Report TR-2024-01, Mar. 2024. [Online]. Available: https://nqr.gov.in/sites/default/files/Annexure%20B_Evidence%20of%20Need.pdf
Sayantan Roy, "Comprehensive Analysis of Advanced AI Security: Attack Vectors, Defense Mechanisms, Ethical Implications & Recent Hacking Vulnerabilities in AI Applications," International Journal of Advanced Security Studies, vol. 15, no. 2, pp. 145-168, 2024. [Online]. Available: https://www.researchgate.net/publication/382841075_Comprehensive_Analysis_of_Advanced_AI_Security_Attack_Vectors
Muhammad Ali; Yim-Fun Hu, et al., "Adversarial Attacks on AI based Intrusion Detection System for Heterogeneous Wireless Communications Networks," IEEE AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), Nov. 2020. [Online]. Available: https://ieeexplore.ieee.org/document/9256597
Merve Ozkan-Okay, Erdal Akin, et al., "A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions," IEEE Security & Privacy, vol. 22, no. 1, pp. 45-58, Jan. 2024. [Online]. Available: https://ieeexplore.ieee.org/document/10403908
William Villegas, et al., "Toward a Comprehensive Framework for Ensuring Security and Privacy in Artificial Intelligence," Electronics 2023, 12(18), 3786. [Online]. Available: https://www.mdpi.com/2079-9292/12/18/3786 DOI: https://doi.org/10.3390/electronics12183786
National Institute of Standards and Technology, "Artificial Intelligence Risk Management Framework (AI RMF 1.0)," NIST AI 100-1, U.S. Department of Commerce, Washington, DC, USA, Tech. Rep., Jan. 2023. [Online]. Available: https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf
Joseph Kate, et al., "Securing the E-commerce AI Ecosystem: Multi-layered Cybersecurity Solutions for Client, Communication, and Database Integrity," International Journal of E-Commerce Security, vol. 12, no. 4, pp. 167-182, 2024. [Online]. Available: https://www.researchgate.net/publication/385812031_Securing_the_E-commerce_AI_Ecosystem_Multi-layered_Cybersecurity_Solutions_for_Client_Communication_and_Database_Integrity
Mahmoud Abouelyazid, et al., "Architectures for AI Integration in Next-Generation Cloud Infrastructure, Development, Security, and Management," International Journal of Information and Cybersecurity, 2019. [Online]. Available: https://publications.dlpress.org/index.php/ijic/article/view/92/84
Tao Zhang, Yi Qin; Qiang Li, et al., "Trusted Artificial Intelligence: Technique Requirements and Best Practices," IEEE International Conference on Cyberworlds (CW), 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9599411 DOI: https://doi.org/10.1109/CW52790.2021.00058
Mohan Baruwal Chhetri, Shahroz Tariq, et al., "Towards Human-AI Teaming to Mitigate Alert Fatigue in Security Operations Centres," ACM Transactions on Internet Technology, Volume 24, Issue 3 Article No.: 12, 2024. [Online]. Available: https://dl.acm.org/doi/full/10.1145/3670009 DOI: https://doi.org/10.1145/3670009
Siva Subrahmanyam Balantrapu, "Future Trends in AI and Machine Learning for Cybersecurity," International Journal of Creative Research in Computer Technology and Design, vol. 5, no. 2, pp. 67-33, 2023. [Online]. Available: https://jrctd.in/index.php/IJRCTD/article/view/67/33
Ashok Manoharan, et al., "Revolutionizing Cybersecurity: Unleashing the Power of Artificial Intelligence and Machine Learning for Next-Generation Threat Detection," International Research Journal of Modernization in Engineering Technology and Science, 2022. [Online]. Available: https://www.researchgate.net/publication/379044498_Revolutionizing_Cybersecurity
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