ARCHIVES
Year 2026 · Volume 7 · Issue 5
Original Article
Development of an AI-Based Model for Predicting Cyber Attacks Using ML and Generative Techniques
Yadlapalli Lalith Nivas1
Dr A S N Chakravarthy2
Chintalapudi Subhash3
1 2 3 Department of Computer Science and Engineering, UCEK, JNTU Kakinada, Andhra Pradesh, India.
Published Online: September-October 2026
Pages: 33-41
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260705006
References
1. S. Dalal, P. Manoharan, U. K. Lilhore, B. Seth, D. M. Alsekait, S. Simaiya, M. Hamdi, and K. Raahemifar, "Extremely boosted neural network for more accurate multi-stage cyber-attack prediction in cloud computing environment," Journal of Cloud Computing, vol. 12, no. 1, pp. 1-18, 2023.
2. P. P. Kundu, T. Truong-Huu, L. Chen, L. Zhou, and S. G. Teo, "Detection and classification of botnet traffic using deep learning with model explanation," Future Generation Computer Systems, vol. 128, pp. 326-340, 2022.
3. M. Al-Hawawreh and N. Moustafa, "Explainable deep learning for attack intelligence and combating cyber-physical attacks," IEEE Access, vol. 12, pp. 11245-11261, 2024.
4. N. Koroniotis, N. Moustafa, E. Sitnikova, and B. Turnbull, "Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset," Future Generation Computer Systems, vol. 100, pp. 779-796, 2019.
5. O. D. Okey, E. U. Udo, R. L. Rosa, D. Z. Rodriguez, and J. H. Kleinschmidt, "Investigating ChatGPT and cybersecurity: A perspective on topic modeling and sentiment analysis," Sensors, vol. 23, no. 15, pp. 1-18, 2023.
6. Y. Meidan, M. Bohadana, Y. Mathov, Y. Mirsky, A. Shabtai, D. Breitenbacher, and Y. Elovici, "N-BaIoT-Network-based detection of IoT botnet attacks using deep autoencoders," IEEE Pervasive Computing, vol. 17, no. 3, pp. 12-22, 2018.
7. B. Hussain, Q. Du, B. Sun, and Z. Han, "Deep learning-based DDoS-attack detection for cyber-physical system over 5G network," IEEE Transactions on Industrial Informatics, vol. 17, no. 2, pp. 860-870, 2021.
8. S. Mahdavifar and A. A. Ghorbani, "Dennes: Deep embedded neural network expert system for detecting cyber-attacks," Neural Computing and Applications, vol. 32, no. 18, pp. 14753-14780, 2020.
9. W. Han, J. Xue, Y. Wang, L. Huang, Z. Kong, and L. Mao, "MalDAE: Detecting and explaining malware based on correlation and fusion of static and dynamic characteristics," Computers & Security, vol. 83, pp. 208-233, 2019.
10. M. M. Alani, "BotStop: Packet-based efficient and explainable IoT botnet detection using machine learning," Journal of Information Security and Applications, vol. 66, pp. 1-14, 2022.
2. P. P. Kundu, T. Truong-Huu, L. Chen, L. Zhou, and S. G. Teo, "Detection and classification of botnet traffic using deep learning with model explanation," Future Generation Computer Systems, vol. 128, pp. 326-340, 2022.
3. M. Al-Hawawreh and N. Moustafa, "Explainable deep learning for attack intelligence and combating cyber-physical attacks," IEEE Access, vol. 12, pp. 11245-11261, 2024.
4. N. Koroniotis, N. Moustafa, E. Sitnikova, and B. Turnbull, "Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset," Future Generation Computer Systems, vol. 100, pp. 779-796, 2019.
5. O. D. Okey, E. U. Udo, R. L. Rosa, D. Z. Rodriguez, and J. H. Kleinschmidt, "Investigating ChatGPT and cybersecurity: A perspective on topic modeling and sentiment analysis," Sensors, vol. 23, no. 15, pp. 1-18, 2023.
6. Y. Meidan, M. Bohadana, Y. Mathov, Y. Mirsky, A. Shabtai, D. Breitenbacher, and Y. Elovici, "N-BaIoT-Network-based detection of IoT botnet attacks using deep autoencoders," IEEE Pervasive Computing, vol. 17, no. 3, pp. 12-22, 2018.
7. B. Hussain, Q. Du, B. Sun, and Z. Han, "Deep learning-based DDoS-attack detection for cyber-physical system over 5G network," IEEE Transactions on Industrial Informatics, vol. 17, no. 2, pp. 860-870, 2021.
8. S. Mahdavifar and A. A. Ghorbani, "Dennes: Deep embedded neural network expert system for detecting cyber-attacks," Neural Computing and Applications, vol. 32, no. 18, pp. 14753-14780, 2020.
9. W. Han, J. Xue, Y. Wang, L. Huang, Z. Kong, and L. Mao, "MalDAE: Detecting and explaining malware based on correlation and fusion of static and dynamic characteristics," Computers & Security, vol. 83, pp. 208-233, 2019.
10. M. M. Alani, "BotStop: Packet-based efficient and explainable IoT botnet detection using machine learning," Journal of Information Security and Applications, vol. 66, pp. 1-14, 2022.
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