NONE 20260504101435130 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON COMPUTERS 1109-2750 2224-2872 Ray-I Chang Department of Engineering Science and Ocean Engineering National Taiwan University No. 1, Sec. 4, Roosevelt Rd., Da'an Dist., Taipei 106319 TAIWAN Dong-Ming Hsieh Department of Engineering Science and Ocean Engineering National Taiwan University No. 1, Sec. 4, Roosevelt Rd., Da'an Dist., Taipei 106319 TAIWAN Wel-Jie Liao Department of Engineering Science and Ocean Engineering National Taiwan University No. 1, Sec. 4, Roosevelt Rd., Da'an Dist., Taipei 106319 TAIWAN Industrial cybersecurity analysis often relies on textual incident descriptions that vary widely in structure, terminology, and level of technical detail. In practical manufacturing environments, the manual interpretation of such problem narratives by domain experts becomes increasingly difficult to sustain as system complexity and data volume grow in Cyber-Physical Systems (CPS). To address this issue, this study examines the use of a deep-learning-based framework, referred to as Deep-Learning-Based Use-Case Recommendation (DLUR), for mapping unstructured industrial problem descriptions to relevant analytical use-cases. The framework combines Doc2Vec-based document representations with Bidirectional Long Short-Term Memory (BiLSTM) models to capture contextual semantic information beyond keyword-level similarity. The resulting representations are further associated with a CPS-oriented hierarchical knowledge structure, enabling recommendations to be interpreted in relation to physical assets and security contexts. Experimental evaluation was conducted using a dataset of 3,548 industrial technical documents. The results indicate that DLUR provides more stable recommendation performance than conventional Support Vector Machine and Random Forest baselines under the examined conditions. These observations suggest that the proposed approach can support analysts during the early stages of cybersecurity assessment, particularly when prior domain experience is limited. 12 31 2025 12 31 2025 317 34 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/23205.2025.24.34 https://wseas.com/journals/computers/2025/a685105-033(2025).pdf H. Kagermann, W. Wahlster, and J. Helbig, Recommendations for Implementing the Strategic Initiative Industry 4.0, Acatech, Germany, 2013. E. A. Lee, “Cyber-physical systems: Design challenges,” Proceedings of the IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC), Orlando, FL, USA, pp. 363–369, 2008, doi: 10.1109/ISORC.2008.25. J. Lee, B. Bagheri, and H.-A. Kao, “A cyberphysical systems architecture for Industry 4.0- based manufacturing systems,” Manufacturing Letters, vol. 3, pp. 18–23, 2015, doi: 10.1016/j.mfglet.2014.12.001. D. Abraham, G. Erceylan, V. Gkioulos, and S. Katsikas, “Towards enhanced cybersecurity in industrial control systems: A systematic review of context-based modeling, digital twins, and machine learning approaches,” International Journal of Information Security, vol. 24, p. 243, 2025, doi: 10.1007/s10207-025-01158-1. A. Fuller, Z. Fan, C. Day, and C. Barlow, “Digital twin: Enabling technologies, challenges, and open research,” IEEE Access, vol. 8, pp. 108952–108971, 2020, doi: 10.1109/ACCESS.2020.2998358. F. Tao, Q. Qi, L. Wang, and A. Nee, “Digital twin-driven smart manufacturing,” IEEE Transactions on Industrial Informatics, vol. 15, no. 4, pp. 2405–2416, 2019, doi: 10.1109/TII.2018.2873186. S. Mittal and O. P. Sangwan, “Big data analytics using data mining techniques: A survey,” in Advanced Informatics for Computing Research, Communications in Computer and Information Science, vol. 955, Springer, Singapore, pp. 243– 254, 2019, doi: 10.1007/978-981-13-3140-4_24. N. Ur-Rahman and J. A. Harding, “Textual data mining for industrial knowledge management and text classification: A business-oriented approach,” Expert Systems with Applications, vol. 39, no. 5, pp. 4729–4739, 2012, doi: 10.1016/j.eswa.2011.09.124. M. M. Aslam, A. Tufail, and M. N. Irshad, “Survey of deep learning approaches for securing industrial control systems: A comparative analysis,” Cyber Security and Applications, vol. 3, Art. no. 100096, 2025, doi: 10.1016/j.csa.2025.100096. F. Ricci, L. Rokach, and B. Shapira, Recommender Systems Handbook, Springer, New York, USA, 2015. X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.- S. Chua, “Neural collaborative filtering,” Proceedings of the 26th International World Wide Web Conference (WWW), Perth, Australia, pp. 173–182, 2017, doi: 10.1145/3038912.3052569. T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” Proceedings of the International Conference on Learning Representations (ICLR), 2013, doi: 10.48550/arXiv.1301.3781. J. Pennington, R. Socher, and C. D. Manning, “GloVe: Global vectors for word representation,” Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1532–1543, 2014, doi: 10.3115/v1/D14- 1162. A. Berenguer, J.-N. Mazón, and D. Tomás, “Word embeddings for retrieving tabular data from research publications,” Machine Learning, vol. 113, pp. 2227–2248, 2024, doi: 10.1007/s10994-023-06472-0. D. Zhang, S. Zhao, Z. Duan, J. Chen, Y. Zhang, and J. Tang, “A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation,” ACM Transactions on Information Systems, vol. 38, no. 1, Art. no. 5, 2020, doi: 10.1145/3361719. T.-Y. Liu, Learning to Rank for Information Retrieval, Springer, Berlin, Germany, 2011. Q. Le and T. Mikolov, “Distributed representations of sentences and documents,” Proceedings of the 31st International Conference on Machine Learning (ICML), Beijing, China, pp. 1188–1196, 2014. S. Hochreiter and J. Schmidhuber, “Long shortterm memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997, doi: 10.1162/neco.1997.9.8.1735. M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,” IEEE Tran sactions on Signal Processing, vol. 45, no. 11, pp. 2673–2681, 1997, doi: 10.1109/78.650093. Y. Liu, Y. Dai, and T. Bakhshi, “Deep learning in cybersecurity: A hybrid BERT–LSTM network for SQL injection attack detection,” IET Information Security, 2024, doi: 10.1049/2024/5565950. J. Lee, E. Lapira, B. Bagheri, and H.-A. Kao, “Recent advances and trends in predictive manufacturing systems in big data environments,” Manufacturing Letters, vol. 1, pp. 38–41, 2013, doi: 10.1016/j.mfglet.2013.09.005. C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval, Cambridge University Press, Cambridge, UK, 2008. D. M. W. Powers, “Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation,” Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37–63, 2011, doi: 10.9735/2229-3981. T. Joachims, “Text categorization with support vector machines: Learning with many relevant features,” Proceedings of the European Conference on Machine Learning (ECML), Chemnitz, Germany, pp. 137–142, 1998. C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, pp. 273– 297, 1995. A. Alharthi, M. Alaryani, and S. Kaddoura, “A comparative study of machine learning and deep learning models in binary and multiclass classification for intrusion detection systems,” Array, vol. 26, Art. no. 100406, 2025, doi: 10.1016/j.array.2025.100406. Y. Kim, “Convolutional neural networks for sentence classification,” Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1746–1751, 2014, doi: 10.3115/v1/D14- 1181. R. Johnson and T. Zhang, “Deep pyramid convolutional neural networks for text categorization,” Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), Vancouver, Canada, pp. 562– 570, 2017, doi: 10.18653/v1/P17-1052. E. Iturbe, C. Dalamagkas, P. RadoglouGrammatikis, E. Rios, and N. Toledo, “A pattern-aware LSTM-based approach for APT detection leveraging a realistic dataset for critical infrastructure security,” Future Generation Computer Systems, vol. 178, Art. no. 108308, 2026, doi: 10.1016/j.future.2025.108308.