TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
submissions@tarupublications.com
Taru Online First Current IssueSpecial IssuesBrowse all Issues
Journal of Information and Optimization Sciences
Open Access Research Article

Integrating natural language processing techniques into cybersecurity workflows : Enhancing cyber protection against emerging threats

, * ,

* Corresponding author · click or hover a name for details

pp. 2329–2338Vol. 46Issue 7October 2025DOI: 10.47974/JIOS-2129XML
Received:
05 Mar 2025
Published Online:
31 Oct 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2129
Pages:
2329–2338

Abstract

Cyberattacks are a constantly evolving danger, requiring cybersecurity to become even more critical. The ever-changing dynamics of attacking threats have finally started to change the terrain of the cyberspace, thus, requiring strong cyber security. The domain of NLP and especially its use in cyber defense has potential to make a significant contribution in addressing these concerns. This research attempts to find any scholarly works that focus on the use of NLP in cyber threat intelligence so that corresponding strategies can be devised to counter novel threats with sophisticated systems that can address the constantly changing dynamics of complex challenges. The paper also tackles the problems of classifying and structuring of many unorganized data sets numbering in terabytes, concentrating the focus on the use of NLP for threat detection, incident response and overall risk assessment, evaluation and compliance control. NLP based cyber systems defence on secondary multi-tiered application architecture NLP frameworks provides stronger positions in countering cyber warfare. This research aims to demonstrate that through NLP, an organization and its critical systems can be digitally fortified, thereby, proving once again the growing necessity of cyber linguistic defense from NLP based frameworks.

Keywords

CybersecurityNatural language processingThreat intelligenceIncident reportsSentiment analysisNamed entity recognition

Subject Classifications

Primary 93A30Secondary 49K15

References

[1] S. Salloum, T. Gaber, S. Vadera, and K. Shaalan, “Phishing email detection using natural language processing techniques: A literature survey,” Procedia Computer Science, vol. 189, pp. 19–28 (2021).
[2] S. Salloum, T. Gaber, S. Vadera, and K. Shaalan, “A systematic literature review on phishing email detection using natural language processing techniques,” IEEE Access, vol. 10, pp. 65703–65727 (2022).
[3] A. Alhogail and A. Alsabih, “Applying machine learning and natural language processing to detect phishing email,” Computers & Security, vol. 110, pp. 102414 (2021).
[4] S. S. Ismail, R. F. Mansour, A. El-Aziz, R. M. Rasha, and A. I. Taloba, “Efficient email spam detection strategy using genetic decision tree processing with NLP features,” Computational Intelligence and Neuroscience, vol. 2022, Article ID 2022 (2022).
[5] M. R. Rahman, R. Mahdavi-Hezaveh, and L. Williams, “A literature review on mining cyberthreat intelligence from unstructured texts,” in Proc. 2020 Int. Conf. Data Mining Workshops (ICDMW), pp. 516–525 (Nov. 2020).
[6] H. Jo, Y. Lee, and S. Shin, “Vulcan: Automatic extraction and analysis of cyber threat intelligence from unstructured text,” Computers & Security, vol. 120, pp. 102763 (2022).
[7] C. Y. Chaw and H. N. Chua, “A framework system using Word Mover’s distance text similarity algorithm for assessing privacy policy compliance,” in IT Convergence and Security: Proc. ICITCS 2021, pp. 79–89, Singapore: Springer (2021).
[8] O. A. Cejas, M. I. Azeem, S. Abualhaija, and L. Briand, “NLP-based automated compliance checking of data processing agreements (DPAs) against GDPR,” IEEE Transactions on Software Engineering (2023).
[9] G. Siewruk and W. Mazurczyk, “Context-aware software vulnerability classification using machine learning,” IEEE Access, vol. 9, pp. 88852–88867 (2021).
[10] S. Sharma and T. Arjunan, “Natural language processing for detecting anomalies and intrusions in unstructured cybersecurity data,” International Journal of Information and Cybersecurity, vol. 7, no. 12, pp. 1–24 (2023).
[11] Z. T. Sworna, Z. Mousavi, and M. A. Babar, “NLP methods in host-based intrusion detection systems: A systematic review and future directions,” Journal of Network and Computer Applications, Article ID 103761 (2023).
[12] N. Tsinganos and I. Mavridis, “Building and evaluating an annotated corpus for automated recognition of chat-based social engineering attacks,” Applied Sciences, vol. 11, no. 22, pp. 10871 (2021).
[13] S. Sharma and A. Jain, “Role of sentiment analysis in social media security and analytics,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 10, no. 5, pp. e1366 (2020).
[14] R. Marinho and R. Holanda, “Automated emerging cyber threat identification and profiling based on natural language processing,” IEEE Access (2023).
[15] F. Spegni, A. Sabatelli, A. Merlo, L. Pepa, L. Spalazzi, and L. Verderame, “A precision cybersecurity workflow for cyber-physical systems: The IoT healthcare use case,” in Proc. European Symp. Research in Computer Security, pp. 409–426, Springer (2022).
[16] M. M. Yamin and B. Katt, “Modeling and executing cyber security exercise scenarios in cyber ranges,” Computers & Security, vol. 116, pp. 102635 (2022).
[17] M. A. Haq, M. A. R. Khan, and M. Alshehri, “Insider threat detection based on NLP word embedding and machine learning,” Intelligent Automation & Soft Computing, vol. 33, no. 1, pp. 619–635 (2022).
[18] S. Silvestri, S. Islam, D. Amelin, G. Weiler, S. Papastergiou, and M. Ciampi, “Cyber threat assessment and management for securing healthcare ecosystems using natural language processing,” International Journal of Information Security, pp. 1–20 (2023).
[19] R. Vinaya Kumar, K. P. Soman, P. Poornachandran, and V. K. Menon, “A deep dive on machine learning for cyber security use cases,” in Machine Learning for Computer and Cyber Security, pp. 122–158, CRC Press (2019).
[20] T. M. Georgescu, “Natural language processing model for automatic analysis of cybersecurity-related documents,” Symmetry, vol. 12, no. 3, pp. 354 (2020).
[21] S. Arvind, B. Unhelkar, S. S. Shankar, P. Chakrabarti, and S. V. Devika, “Advancing cyber threat detection through deep learning in management information systems,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 8, pp. 2409–2417 (2024), doi: 10.47974/JDMSC-2014.
[22] S. K. Mandal, R. Singh, N. Chandu, D. Mehta, S. K. Henge, D. Kothapeta, C. K. Hinge, A. Sharma, and T. Hussain, “Evolutionary multi-authentication cryptographic key implications for secure data access control,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 5-B, pp. 1865–1874 (2025), doi: 10.47974/JDMSC-2363.

Views: 93Downloads: 69Citations: 0