An NLP-Based Approach to Fortifying Cyber Defenses

Authors

  • Albia Maqbool Department of Computer Sciences Faculty of Computing and Information Technology, Northern Border University, Kingdom of Saudi Arabia
  • Raghav Mehra Professor, Department AI/ML, Chandigarh University
  • Jihane Ben Slimane Department of Computer Sciences Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia, National Engineering School of Tunis, LR11ES20 Analysis Design and Control of Systems Laboratory, University of Tunis El Manar, Tunis, Tunisia
  • Eman H. Abd-Elkawy Department of Computer Sciences, Faculty of Computing & IT, Northern Border University, Saudi Arabia, Department of Mathematics and Computer Science, Faculty of Science, Beni-Suef University, Beni-Suef 62521, Egypt
  • Nargis Parveen Lecturer, Department of Computer Science, Faculty of Computing and Information Technology, Northern Border University, Kingdom of Saudi Arabia
  • Bindiya Ahuja Professor, Department CSE, Lingaya’s Vidyapeeth
  • Greeshma G. S. Assistant Professor, Department -Computer science and Engineering Galgotias university

Keywords:

ecosystems, NLP, deviations, OTX, fortifying, cybersecurity

Abstract

This research introduces an innovative approach to fortifying cybersecurity defenses through behavior-based anomaly detection and response mechanisms. Leveraging NLP techniques, our LLM analyzes system logs and Websites to identify anomalous patterns indicative for classification of the type of attack. On analysis of the datasets “Exploits Database”,” GDELT” and “OTX”, the system accurately detects deviations and dynamically suggests security measures based on the severity and class of attack. Evaluation on diverse datasets showcases the model's superiority over traditional signature-based methods, emphasizing its efficacy in identifying novel and sophisticated cyber threats. The model has an accuracy of 71.22% in classifying large amount of unlabeled data. This research contributes valuable insights to the ongoing efforts in fortifying digital ecosystems against evolving cybersecurity challenges.

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References

S. Srinivasan and P. Deepalakshmi, "Enhancing the security in cyber-world by detecting the botnets using ensemble classification based machine learning," Measurement: Sensors, vol. 25, p. 100624, 2023.

M. Amine Ferrag, M. Ndhlovu, N. Tihanyi, L. C. Cordeiro, M. Debbah and T. Lestable, "Revolutionizing Cyber Threat Detection with Large Language Models," arXiv e-prints, p. arXiv–2306, 2023.

W. S. Admass, Y. Y. Munaye and A. A. Diro, "Cyber Security and Applications".

M. E. Dapel, M. Asante, C. D. Uba and M. O. Agyeman, "Artificial Intelligence Techniques in Cybersecurity Management," in Cybersecurity in the Age of Smart Societies: Proceedings of the 14th International Conference on Global Security, Safety and Sustainability, London, September 2022, 2023.

Ó. Mogollón-Gutiérrez, J. C. Sancho Núñez, M. Á. Vegas and A. Caro Lindo, "A Novel Ensemble Learning System for Cyberattack Classification.," Intelligent Automation & Soft Computing, vol. 37, 2023.

D.-W. Kim, G.-Y. Shin and M.-M. Han, "Anomaly Detection Based on Discrete Wavelet Transformation for Insider Threat Classification.," Comput. Syst. Sci. Eng., vol. 46, p. 153–164, 2023.

F. S. Alrayes, N. Alotaibi, J. S. Alzahrani, S. Alazwari, A. Alhogail, A. M. Al-Sharafi, M. Othman and M. A. Hamza, "Enhanced Gorilla Troops Optimizer with DL Enabled Cybersecurity Threat Detection," Computer Systems Science & Engineering, vol. 45, 2023.

S. Silvestri, S. Islam, S. Papastergiou, C. Tzagkarakis and M. Ciampi, "A Machine Learning Approach for the NLP-Based Analysis of Cyber Threats and Vulnerabilities of the Healthcare Ecosystem," Sensors, vol. 23, p. 651, 2023.

M. Al-Essa, G. Andresini, A. Appice and D. Malerba, "PANACEA: a neural model ensemble for cyber-threat detection," Machine Learning, p. 1–44, 2024.

N. A. M. Razali, N. A. Malizan, N. A. Hasbullah, M. Wook, N. M. Zainuddin, K. K. Ishak, S. Ramli and S. Sukardi, "Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique," IEEE Access, vol. 11, p. 17151–17164, 2023.

K. U. Abinesh Kamal and S. V. Divya, "Integrated threat intelligence platform for security operations in organizations," Automatika, vol. 65, p. 401–409, 2024.

M. H. Kabir, A. Hasnat, A. J. Mahdi, M. N. Hasan, J. A. Chowdhury and I. M. Fahim, "Enhancing Insider Malware Detection Accuracy with Machine Learning Algorithms," Engineering Proceedings, vol. 58, p. 104, 2023.

A. Darem, A. A. Alhashmi, T. M. Alkhaldi, A. M. Alashjaee, S. M. Alanazi and S. A. Ebad, "Cyber threats classifications and countermeasures in banking and financial sector," IEEE Access, vol. 11, p. 125138–125158, 2023.

O. Cherqi, Y. Moukafih, M. Ghogho and H. Benbrahim, "Enhancing Cyber Threat Identification in Open-Source Intelligence Feeds through an Improved Semi-Supervised Generative Adversarial Learning Approach with Contrastive Learning," IEEE Access, 2023.

P. Das, M. R. Al Asif, S. Jahan, R. Khondoker, K. Ahmed and F. M. Bui, "STRIDE-Based Cybersecurity Threat Modeling, Risk Assessment and Treatment of an Infotainment High Performance Computing (HPC) System," 2024.

"Exploits database," Offsec, 4 november 2009. [Online]. Available: https://exploit-db.com/. [Accessed 27 Jan 2024].

M. Singhal, N. Kumarswamy, S. Kinhekar and S. Nilizadeh, "Cybersecurity Misinformation Detection on Social Media: Case Studies on Phishing Reports and Zoom’s Threat," in Proceedings of the International AAAI Conference on Web and Social Media, 2023.

W. Chung, Y. Zhang and J. Pan, "A theory-based deep-learning approach to detecting disinformation in financial social media," Information Systems Frontiers, vol. 25, p. 473–492, 2023.

N. Sun, M. Ding, J. Jiang, W. Xu, X. Mo, Y. Tai and J. Zhang, "Cyber Threat Intelligence Mining for Proactive Cybersecurity Defense: A Survey and New Perspectives," IEEE Communications Surveys & Tutorials, 2023.

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Published

24.03.2024

How to Cite

Maqbool, A. ., Mehra, R. ., Slimane, J. B. ., Abd-Elkawy, E. H. ., Parveen, N. ., Ahuja, B. ., & G. S., G. . (2024). An NLP-Based Approach to Fortifying Cyber Defenses. International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 390–399. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5078

Issue

Vol. 12 No. 19s (2024)

Section

Research Article

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