Revolutionizing Cybersecurity: The GPT-2 Enhanced Attack Detection and Defense (GEADD) Method for Zero-Day Threats
Keywords:
Zero-Day Attack Detection, GPT-2 Model, Metaheuristic Optimization, Cybersecurity, Deep Learning
Abstract
The escalating sophistication of cyber threats, particularly zero-day attacks, necessitates advanced detection methodologies in cybersecurity. This study introduces the GPT-2 Enhanced Attack Detection and Defense (GEADD) method, an innovative approach that integrates the GPT-2 model with metaheuristic optimization techniques for enhanced detection of zero-day threats. The GEADD method encompasses data preprocessing, Equilibrium Optimization (EO)-based feature selection, and Salp Swarm Algorithm-Based Optimization (SABO) for hyperparameter tuning, culminating in a robust framework capable of identifying and classifying zero-day attacks with high accuracy. Through a comprehensive evaluation using standard datasets, the GEADD method demonstrates superior performance in detecting zero-day threats compared to existing models, highlighting its potential as a significant contribution to the field of cybersecurity. This study not only presents a novel application of deep learning for cyber threat detection but also sets a foundation for future research in AI-driven cybersecurity solutions
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Bilge, L., & Dumitraş, T. (2012). Before we knew it: An empirical study of zero-day attacks in the real world. In Proceedings of the 2012 ACM Conference on Computer and Communications Security (CCS '12), ACM, pp. 833–844.
Bridges, R. A., Oesch, S., Verma, M. E., Iannacone, M. D., Huffer, K. M. T., Jewell, B., Nichols, J. A., Weber, B., Beaver, J. M., Smith, J. M., Scofield, D., Miles, C., Plummer, T., Daniell, M., & Tall, A. M. (2021). Beyond the hype: A real-world evaluation of the impact and cost of machine learning-based malware detection. arXiv:2012.09214.
Comar, P. M., Liu, L., Saha, S., Tan, P.-N., & Nucci, A. (2013). Combining supervised and unsupervised learning for zero-day malware detection. In 2013 Proceedings IEEE INFOCOM, pp. 2022–2030. http://dx.doi.org/10.1109/INFCOM.2013.6567003
Drozdenko B., and Powell M. (2022). Utilizing Deep Learning Techniques to Detect Zero-Day Exploits in Network Traffic.
Drozdenko, B., & Powell, M. (2022). Utilizing Deep Learning Techniques to Detect Zero-Day Exploits in Network Traffic Flows. In IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (pp. 0163-0172).
Google. (n.d.). Project Zero. Retrieved from https://googleprojectzero.blogspot.com/p/0day.html
Hindy, H., Atkinson, R., Tachtatzis, C., Colin, J.-N., Bayne, E., & Bellekens, X. (2020). Utilising deep learning techniques for effective zero-day attack detection. Electronics, 9(10). http://dx.doi.org/10.3390/electronics9101684
Huda, S., Miah, S., Hassan, M. M., Islam, R., Yearwood, J., Alrubaian, M., & Almogren, A. (2017). Defending unknown attacks on cyber-physical systems by semi-supervised approach and available unlabeled data. Inform. Sci., 379, 211–228. http://dx.doi.org/10.1016/j.ins.2016.09.041
Ibrahim H.B., Aslan H.K., Elsayed M.S., Jurcut A.D., and Azer M.A. (2023). Anomaly Detection of Zero-Day Attacks Based on CNN and Regularization Techniques. Electronics.
Kim, J.-Y., Bu, S.-J., & Cho, S.-B. (2018). Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders. Inform. Sci., 460–461, 83–102. http://dx.doi.org/10.1016/j.ins.2018.04.092
Mirsky, Y., Doitshman, T., Elovici, Y., & Shabtai, A. (2018). Kitsune: An ensemble of autoencoders for online network intrusion detection. NDSS.
Peppes, N., Alexakis, T., Adamopoulou, E., & Demestichas, K. (2023). The Effectiveness of Zero-Day Attacks Data Samples Generated via GANs on Deep Learning Classifiers. Sensors, 23(2), 900.
Ponemon Sullivan Privacy Report. (2020). The economic value of prevention in the cybersecurity lifecycle.
Popoola, S. I., Ande, R., Adebisi, B., Gui, G., Hammoudeh, M., & Jogunola, O. (2021). Federated deep learning for zero-day botnet attack detection in IoT-edge devices. IEEE Internet of Things Journal, 9(5), 3930-3944.
Priya, S., & Annie Uthra, R. (2021). An Effective Deep Learning-Based Variational Autoencoder for Zero-Day Attack Detection Model. In Inventive Systems and Control: Proceedings of ICISC 2021 (pp. 205-212). Springer Singapore.
Roshan, K., & Zafar, A. (2021). An Optimized Auto-Encoder based Approach for Detecting Zero-Day Cyber-Attacks in Computer Network. In 2021 5th International Conference on Information Systems and Computer Networks (ISCON) (pp. 1-6). IEEE.
Samha A.K., Malik N., Sharma D., and Dutta P. (2023). Intrusion Detection System Using Hybrid Convolutional Neural Network. Mobile Networks and Applications.
Sara J.J., and Hossain S. (2023). Static Analysis Based Malware Detection for Zero-Day Attacks in Android Applications. In 2023 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD).
Shen, S., Cai, C., Li, Z., Shen, Y., Wu, G., & Yu, S. (2024). Deep Q-network-based heuristic intrusion detection against edge-based SIoT zero-day attacks. Applied Soft Computing, 150, 111080.
Swathy Akshaya M., and Padmavathi G. (2022). Zero-Day Attack Path Identification using Probabilistic and Graph Approach based Back Propagation Neural Network in Cloud. Mathematical Statistician and Engineering Applications.
Wu, Y., Hu, Y., Wang, J., Feng, M., Dong, A., & Yang, Y. (2024). An Active Learning Framework Using Deep Q-Network for Zero-day Attack Detection. Computers & Security, 103713.
Zhou, Q., & Pezaros, D. (2021). Evaluation of machine learning classifiers for zero-day intrusion detection – an analysis on CIC-aws-2018 dataset. arXiv:1905.03685.
Published
2024-04-30
How to Cite
[1]
R. Jones and M. Omar, “Revolutionizing Cybersecurity: The GPT-2 Enhanced Attack Detection and Defense (GEADD) Method for Zero-Day Threats”, INJIISCOM, vol. 5, no. 2, pp. 178-191, Apr. 2024.
Section
Articles
