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Title: :  Generative models for creating honeypot system to lure cyber attackers
PaperId: :  27416
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 4 2025
DUI:    16.0415/IJARIIE-27416
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
SHRUSHTI K NT JOHN INSTITUTION OF TECHNOLOGY
M SELVAM T JOHN INSTITUTION OF TECHNOLOGY

Abstract

cyber security
Generative Adversarial Networks (GANs) can be employed to create realistic decoy network traffic that mimics legitimate user behavior to attract cyber attackers. Variational Autoencoders (VAEs) are useful for generating synthetic system logs that simulate typical user activity and hide deceptive elements. Large Language Models (LLMs) such as GPT can be used to dynamically generate realistic command-line responses or fake service banners to engage attackers. Deep generative models enable the creation of high-fidelity honeypot environments that closely resemble real systems in both behavior and appearance. Synthetic data generation techniques allow the creation of fake user credentials, file systems, and communication patterns to enhance the believability of the honeypot. AI-generated network protocols can simulate legitimate service communications, helping lure attackers into interacting with the decoy system.
Threats to information security, such as malware, are constantly evolving. The number of malware cases reported increased by over six million in 2014. The amount of Trojan Horse malware is highest, while the increase in Adware malware is most noticeable. It is believed that security system equipment such as firewalls, antivirus software, and Asa signature- based security systems cannot identify malware. This happens because computer malware is becoming more and more prevalent and because the number of signatures is always increasing. Without signature-based security measures, it is difficult to detect new tactics, viruses, or worms used by attackers. Another alternative for malware detection is to use machine learning in combination with honeypots. Honeypots can be used to catch suspicious packages, while machine learning can identify spyware by classifying it. Support The classification algorithms used are Support Vector Machines ( SVM) and Decision Trees, respectively. As a means of malware detection, we propose utilizing design elements in this study. We offered the design suggestion and detailed the experimental technique to be used. Threats to information security, such as malware, are constantly evolving. In 2014, there were about six million new instances of malware reported. The amount of Trojan Horse malware is highest, while the increase in Adware malware is most noticeable. It is often believed that security system devices focused on antivirus signatures, routers, and intrusion detection systems cannot identify malware. This happens because computer malware is becoming more and more prevalent and because the number of signatures is always increasing. Without signature-based security measures, it is difficult to detect new tactics, viruses, or worms used by attackers. Another alternative for malware detection is to use machine learning in combination with honeypots. Honeypots can be used to catch suspicious packages, while machine learning can identify viruses by classifying it. We use Decision Trees and Support Vector Machines (SVMs) for classification. As a means of malware detection, we propose utilizing design elements in this study. In addition to providing the architectural plan, we detailed the experimental technique that will be used.

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IJARIIE SHRUSHTI K N, and M SELVAM . "Generative models for creating honeypot system to lure cyber attackers" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 4 2025 Page 4039-4046
MLA SHRUSHTI K N, and M SELVAM . "Generative models for creating honeypot system to lure cyber attackers." International Journal Of Advance Research And Innovative Ideas In Education 11.4(2025) : 4039-4046.
APA SHRUSHTI K N, & M SELVAM . (2025). Generative models for creating honeypot system to lure cyber attackers. International Journal Of Advance Research And Innovative Ideas In Education, 11(4), 4039-4046.
Chicago SHRUSHTI K N, and M SELVAM . "Generative models for creating honeypot system to lure cyber attackers." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 4 (2025) : 4039-4046.
Oxford SHRUSHTI K N, and M SELVAM . 'Generative models for creating honeypot system to lure cyber attackers', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 4, 2025, p. 4039-4046. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Generative_models_for_creating_honeypot_system_to_lure_cyber_attackers_ijariie27416.pdf (Accessed : ).
Harvard SHRUSHTI K N, and M SELVAM . (2025) 'Generative models for creating honeypot system to lure cyber attackers', International Journal Of Advance Research And Innovative Ideas In Education, 11(4), pp. 4039-4046IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Generative_models_for_creating_honeypot_system_to_lure_cyber_attackers_ijariie27416.pdf (Accessed : )
IEEE SHRUSHTI K N, and M SELVAM , "Generative models for creating honeypot system to lure cyber attackers," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 4, pp. 4039-4046, Jul-Aug 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/Generative_models_for_creating_honeypot_system_to_lure_cyber_attackers_ijariie27416.pdf [Accessed : ].
Turabian SHRUSHTI K N, and M SELVAM . "Generative models for creating honeypot system to lure cyber attackers." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 4 ().
Vancouver SHRUSHTI K N, and M SELVAM . Generative models for creating honeypot system to lure cyber attackers. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(4) : 4039-4046. Available from: https://ijariie.com/AdminUploadPdf/Generative_models_for_creating_honeypot_system_to_lure_cyber_attackers_ijariie27416.pdf
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