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International Research Journal of Advanced Engineering and Technology

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Adaptive AI-Driven Intrusion Detection for Secure Industry 5.0 Smart Manufacturing Environments

DOI https://doi.org/10.55640/
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Dr. Ahsan Raza 1 , Dr. Mahnoor Fatima 1 ,
4 Department of Computer Science and Intelligent Systems, National Institute of Technology, Lahore, Pakistan
4 Department of Cybersecurity and Information Assurance, Institute of Emerging Technologies, Islamabad, Pakistan

Abstract

Industry 5.0 smart manufacturing environments integrate industrial Internet of Things (IIoT) devices, cyber-physical systems, cloud services, edge computing, intelligent automation, and human-centric production processes. This convergence increases operational intelligence but simultaneously expands the attack surface and introduces heterogeneous, dynamic, and distributed security requirements. Conventional intrusion detection systems (IDSs), particularly static rule-based and signature-oriented approaches, face limitations in recognizing evolving attacks, zero-day behaviors, distributed threats, and context-dependent anomalies. This research proposes an adaptive AI-driven intrusion detection framework for Industry 5.0 smart manufacturing environments by synthesizing machine learning, clustering, real-time detection, cloud-edge security, and threat-action analysis principles from the provided literature. The proposed methodology combines preprocessing, behavioral feature extraction, adaptive clustering, supervised anomaly classification, threat-context analysis, and continuous model updating. K-means-based behavioral grouping provides an initial mechanism for identifying deviations, while adaptive learning improves responsiveness to changing industrial traffic. The framework further incorporates cloud-edge considerations to reduce latency and support distributed security decisions. The analysis indicates that adaptive AI can improve the conceptual resilience, scalability, and responsiveness of intrusion detection compared with static approaches, although computational overhead, data quality, model drift, false positives, and deployment complexity remain significant limitations. The study positions adaptive intrusion detection as a foundational security mechanism for trustworthy Industry 5.0 manufacturing infrastructures.

Keywords

Industry 5.0, Intrusion Detection, Artificial Intelligence, Machine Learning

References

A. Patel, M. Taghavi, K. Bakhtizyari, and J. C. Junior, “An intrusion detection and prevention system in cloud comuting: A systematic review,” J. Netw. Comput. Appl., vol. 36, no. 1, pp. 25–41, 2012. doi: 10.1016/j.jnca.2012.08.007.
A. Tchagna Kouanou, D. Tchiotsop, R. Tchinda, C. Tchito Tchapga, A. N. Kengnou Telem and R. Kengne, “A machine learning algorithm for biomedical images compression using orthogonal transforms,” Int. J. Image, Graph. Signal Process., vol. 10, no. 11, pp. 38–53, 2018. doi: 10.5815/ijigsp.2018.11.05.
A. Tchagna Kouanou, D. Tchiotsop, T. Fozin Fonzin, B. Mounmo, and R. Tchinda, “Real-time image compression system using an embedded board,” Sci. J. Circuit., Syst. Signal Process., vol. 7, no. 4, pp. 81–86, 2019. doi: 10.11648/j.cssp.20180704.11.
A. Uddin and L. Hasan, “Design and analysis of real-time network intrusion detection and prevention system using open source tools,” Int. J. Comput. Appl., vol. 138, no. 7, pp. 6–11, 2016. doi: 10.5120/ijca2016908921.
B. S. K. Devi and T. Subbulakshmi, “Intrusion detection and prevention of DDoS attacks in cloud computing environments: A review of issues and current methods,” Int. J. Cloud Comput., vol. 12, no. 5, pp. 450–481, 2023. doi: 10.1504/IJCC.2023.134001.
C. M. Ou, “Host-based intrusion detection systems adapted from agent-based artificial immune systems,” J. Netw. Comput. Appl., vol. 6, no. 1, pp. 25–41, 2012. doi: 10.1016/j.neucom.2011.07.031.
D. Singh and S. Lata, “Intrusion detection system in cloud environment: Litterature survey & future research directions,” Int. J. Inf. Manag. Data Insights, vol. 2, no. 2022, pp. 1–14, 2022. doi: 10.1016/j.jjimei.2022.100134.
H. Jin et al., “A VMM-based Intrusion prevention system in cloud computing environment,” J. Supercomput., Spinger, vol. 10, no. 3, pp. 1–19, 2011. doi: 10.1007/s11227-011-0608-2.
H. Zhang, G. Shen, C. Guo, Y. Gui, and C. Jiang, “EX-Action: Automatically extracting threat actions from cyber threat intelligence report based on multimodal learning,” Secur. Commun. Netw., vol. 2021, no. 2, pp. 1–12, 2021. doi: 10.1155/2021/5586335.
M. L. Hernandez-Jaimes, A. Martinez-Cruz, K. A. Ramirez-Gutièrrez, and C. Feregrino-Uribe, “Artificial intelligence for IoMT security: A review of intrusion detection systems, attacks, datasets and Cloud-Forg-Edge architectures,” Internet Things, no. 23, 2023, Art. no. 100887. doi: 10.1016/j.iot.2023.100887.
MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proc. 5th Berkeley Symp. Mathemat. Statist. Probabil., 1967, pp. 281–297.
MathWorks, “k-Means Clustering,” 2024. Accessed: Nov. 19 2024. [Online]. Available: https://www.mathworks.com/help/stats/k-means-clustering.html
N. Eddermoug, A. Mansour, M. Azmi, M. Sadik, E. Sabir and H. Bahassi, “A litterature review on attack prevention and prefiling in cloud computing,” in Proc. Comput. Sci. 1st Int. Workshop Human-Centr. Innov. Computat. Intell., vol. 220, no. 2023, pp. 970–977, 2023. doi: 10.1016/j.procs.2023.03.134.
OWASP, “API7:2023 Server Side Request Forgery,” 2023. Accessed: Nov. 19 2024. [Online]. Available: https://owasp.org/API-Security/editions/2023/en/0xa7-server-side-request-forgery/
P. N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. UK: Addison-Wesley, Reading, 2005. doi: 10.1007/978-0-387-47716-9.
S. Lloyd, “Least squares quantization in PCM,” Int. Electr. Electron. Eng. Trans., Inf., vol. 28, no. 2, pp. 129–137, 1982. doi: 10.1109/TIT.1982.1056489.
T. Sowmya and E. A. Mary Anita, “A comprehensive review of AI based intrusion detection system,” Measur.: Sens., vol. 28, no. 2023, pp. 1–13, 2023. doi: 10.1016/j.measen.2023.100827.
W. Junjie, “Cluster analysis and K-means clustering: An introduction. Advances in K-means clustering: A data mining thinking,” in Advances in K-Means Clustering. Berlin, Heidelberg: Springer, 2012, pp. 1–16. doi: 10.1007/978-3-642-29807-3_1.
Govindarajan, V., Ahmed, F., Kamaluddin, K. et al. Secure Risk Net: An Advanced AI-Driven Framework for Intelligent Security Risk Detection in Heterogeneous Cloud-Fog Computing Networks. Int J Comput Intell Syst 19, 74 (2026).
K. Ramamurthy, N. Bellamkonda and N. Amanmadov, "ScalePulse: Combinatorial Llm Framework for Scalability Constraint," SoutheastCon 2026, Huntsville, AL, USA, 2026, pp. 1-6, doi: 10.1109/SoutheastCon63549.2026.11476075
Philip, P. G. (2026). Artificial Intelligence–Driven Intelligent Project Management: an integrated framework for planning, scheduling and control in engineering and construction projects. Journal of Engineering and Artificial Intelligence, 02(02), 01–09. https://doi.org/10.64142/jeai.2.2.50

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