Application of Honeypot in Network Security for Detecting Cyber Attacks on IT Infrastructure
##plugins.themes.bootstrap3.article.main##
Abstract
The high security risks that are susceptible to hacking and exploitation by malicious actors to steal data or information often arise due to a lack of awareness regarding the critical importance of implementing deceptive network security using honeypots. Negligence can create vulnerabilities that are easily exploited, allowing attackers to initiate breaches. A notable network security approach involves using Honeypots, a method that creates a decoy server to mimic an authentic one. Honeypots are deliberately engineered to attract the attention of cyber attackers and facilitate their access to the trap server, thereby enabling the monitoring and analysis of their activities without compromising the integrity of the primary server. To achieve optimal network security, comprehensive testing of Honeypots is essential. This testing process serves as a fundamental metric in evaluating the efficacy and performance of Honeypot systems in mitigating cyber threats.
##plugins.themes.bootstrap3.article.details##
Section
Articles
References
[1] J. Williams, M. Edwards, and J. Gardiner, “Time-to-Lie: Identifying Industrial Control System Honeypots Using the Internet Control Message Protocol,” 2024. [Online]. Available: https://arxiv.org/abs/2410.17731
[2] A. Albaseer, N. Abdi, M. Abdallah, M. Qaraqe, and S. Alkuwari, “FedPot: A Quality-Aware Collaborative and Incentivized Honeypot-Based Detector for Smart Grid Networks,” 2024. [Online]. Available: https://arxiv.org/abs/2407.02845
[3] N. Kaur and L. Gupta, “Explainable AI for Securing Healthcare in IoT-Integrated 6G Wireless Networks,” 2025. [Online]. Available: https://arxiv.org/abs/2505.14659
[4] P. B. Lopez, P. Nespoli, and M. G. Perez, “Cyber Deception Reactive: TCP Stealth Redirection to On-Demand Honeypots,” 2024. [Online]. Available: https://arxiv.org/abs/2402.09191
[5] M. Kahlhofer and S. Rass, “Application Layer Cyber Deception Without Developer Interaction,” in 2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), IEEE, Jul. 2024, pp. 416–429. doi: 10.1109/EuroSPW61312.2024.00053.
[6] Y. L. Aung et al., “HoneyWin: High-Interaction Windows Honeypot in Enterprise Environment,” 2025. [Online]. Available: https://arxiv.org/abs/2505.00465
[7] U. Ubaidillah, T. Taryo, and A. Hindasyah, “Analisis dan Implementasi Honeypot Honeyd Sebagai Low Interaction Terhadap Serangan Distributed Denial Of Service (DDOS) dan Malware,” JTIM : Jurnal Teknologi Informasi dan Multimedia, vol. 5, no. 3, pp. 208–217, Oct. 2023, doi: 10.35746/jtim.v5i3.405.
[8] Y. Wang, Z. Su, A. Benslimane, Q. Xu, M. Dai, and R. Li, “Collaborative Honeypot Defense in UAV Networks: A Learning-Based Game Approach,” 2023. [Online]. Available: https://arxiv.org/abs/2211.01772
[9] A. Said, “On explaining recommendations with Large Language Models: a review,” Front Big Data, vol. 7, Jan. 2025, doi: 10.3389/fdata.2024.1505284.
[10] Y. Wang, T. Gu, Y. Teng, Y. Wang, and X. Ma, “HoneypotNet: Backdoor Attacks Against Model Extraction,” 2025. [Online]. Available: https://arxiv.org/abs/2501.01090
[11] M. Nawrocki, J. Kristoff, R. Hiesgen, C. Kanich, T. C. Schmidt, and M. Wählisch, “SoK: A Data-driven View on Methods to Detect Reflective Amplification DDoS Attacks Using Honeypots,” in 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P), IEEE, Jul. 2023, pp. 576–591. doi: 10.1109/EuroSP57164.2023.00041.
[12] J. Landsborough, N. C. Rowe, T. D. Nguyen, and S. Fugate, “WiP: Deception-in-Depth Using Multiple Layers of Deception,” 2024. [Online]. Available: https://arxiv.org/abs/2412.16430
[13] A. Javadpour, F. Ja’fari, T. Taleb, M. Shojafar, and C. Benzaïd, “A comprehensive survey on cyber deception techniques to improve honeypot performance,” Comput Secur, vol. 140, p. 103792, May 2024, doi: 10.1016/j.cose.2024.103792.
[14] K. Highnam, Z. Hanif, E. Van Vogt, S. Parbhoo, S. Maffeis, and N. R. Jennings, “Adaptive Experimental Design for Intrusion Data Collection,” 2023. [Online]. Available: https://arxiv.org/abs/2310.13224
[15] L. Sousa, J. Cecílio, P. Ferreira, and A. Oliveira, “Reconfigurable and Scalable Honeynet for Cyber-Physical Systems,” 2024. [Online]. Available: https://arxiv.org/abs/2404.04385
[16] Y. Otoum, A. Asad, and A. Nayak, “Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security,” 2025. [Online]. Available: https://arxiv.org/abs/2504.16226
[17] Z. Peng, Y. He, J. Ni, and B. Niu, “Bypassing DARCY Defense: Indistinguishable Universal Adversarial Triggers,” 2024. [Online]. Available: https://arxiv.org/abs/2409.03183
[18] H. Q. Ngo, M. Guo, and H. Nguyen, “Catch Me if You Can: Effective Honeypot Placement in Dynamic AD Attack Graphs,” 2023. [Online]. Available: https://arxiv.org/abs/2312.16820
[19] Irfan Murti Raazi, Ima Dwitawati, and Putri Nabila, “Uji Vulnerability Assessment Dalam Mengetahui Tingkat Keamanan Web Aplikasi Sistem Informasi Laporan Diskominfo Dan Sandi Aceh,” J-INTECH: Journal Of Information Technology, vol. 4, no. 1, pp. 1–15, Feb. 2023, doi: 10.22373/j-intech.v4i1.2409.
[20] A. Ebunoluwa and A. James, “AI-Powered Honeypots: Enhancing Deception Technologies for Cyber Defense,” Jun. 2025.
[21] M. B. -, “AI-enhanced Honeypots for Zero-Day Exploit Detection and Mitigation,” International Journal For Multidisciplinary Research, vol. 6, no. 6, Dec. 2024, doi: 10.36948/ijfmr.2024.v06i06.32866.
[22] Z. Zhang et al., “Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space,” 2025. [Online]. Available: https://arxiv.org/abs/2505.15778
[2] A. Albaseer, N. Abdi, M. Abdallah, M. Qaraqe, and S. Alkuwari, “FedPot: A Quality-Aware Collaborative and Incentivized Honeypot-Based Detector for Smart Grid Networks,” 2024. [Online]. Available: https://arxiv.org/abs/2407.02845
[3] N. Kaur and L. Gupta, “Explainable AI for Securing Healthcare in IoT-Integrated 6G Wireless Networks,” 2025. [Online]. Available: https://arxiv.org/abs/2505.14659
[4] P. B. Lopez, P. Nespoli, and M. G. Perez, “Cyber Deception Reactive: TCP Stealth Redirection to On-Demand Honeypots,” 2024. [Online]. Available: https://arxiv.org/abs/2402.09191
[5] M. Kahlhofer and S. Rass, “Application Layer Cyber Deception Without Developer Interaction,” in 2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), IEEE, Jul. 2024, pp. 416–429. doi: 10.1109/EuroSPW61312.2024.00053.
[6] Y. L. Aung et al., “HoneyWin: High-Interaction Windows Honeypot in Enterprise Environment,” 2025. [Online]. Available: https://arxiv.org/abs/2505.00465
[7] U. Ubaidillah, T. Taryo, and A. Hindasyah, “Analisis dan Implementasi Honeypot Honeyd Sebagai Low Interaction Terhadap Serangan Distributed Denial Of Service (DDOS) dan Malware,” JTIM : Jurnal Teknologi Informasi dan Multimedia, vol. 5, no. 3, pp. 208–217, Oct. 2023, doi: 10.35746/jtim.v5i3.405.
[8] Y. Wang, Z. Su, A. Benslimane, Q. Xu, M. Dai, and R. Li, “Collaborative Honeypot Defense in UAV Networks: A Learning-Based Game Approach,” 2023. [Online]. Available: https://arxiv.org/abs/2211.01772
[9] A. Said, “On explaining recommendations with Large Language Models: a review,” Front Big Data, vol. 7, Jan. 2025, doi: 10.3389/fdata.2024.1505284.
[10] Y. Wang, T. Gu, Y. Teng, Y. Wang, and X. Ma, “HoneypotNet: Backdoor Attacks Against Model Extraction,” 2025. [Online]. Available: https://arxiv.org/abs/2501.01090
[11] M. Nawrocki, J. Kristoff, R. Hiesgen, C. Kanich, T. C. Schmidt, and M. Wählisch, “SoK: A Data-driven View on Methods to Detect Reflective Amplification DDoS Attacks Using Honeypots,” in 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P), IEEE, Jul. 2023, pp. 576–591. doi: 10.1109/EuroSP57164.2023.00041.
[12] J. Landsborough, N. C. Rowe, T. D. Nguyen, and S. Fugate, “WiP: Deception-in-Depth Using Multiple Layers of Deception,” 2024. [Online]. Available: https://arxiv.org/abs/2412.16430
[13] A. Javadpour, F. Ja’fari, T. Taleb, M. Shojafar, and C. Benzaïd, “A comprehensive survey on cyber deception techniques to improve honeypot performance,” Comput Secur, vol. 140, p. 103792, May 2024, doi: 10.1016/j.cose.2024.103792.
[14] K. Highnam, Z. Hanif, E. Van Vogt, S. Parbhoo, S. Maffeis, and N. R. Jennings, “Adaptive Experimental Design for Intrusion Data Collection,” 2023. [Online]. Available: https://arxiv.org/abs/2310.13224
[15] L. Sousa, J. Cecílio, P. Ferreira, and A. Oliveira, “Reconfigurable and Scalable Honeynet for Cyber-Physical Systems,” 2024. [Online]. Available: https://arxiv.org/abs/2404.04385
[16] Y. Otoum, A. Asad, and A. Nayak, “Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security,” 2025. [Online]. Available: https://arxiv.org/abs/2504.16226
[17] Z. Peng, Y. He, J. Ni, and B. Niu, “Bypassing DARCY Defense: Indistinguishable Universal Adversarial Triggers,” 2024. [Online]. Available: https://arxiv.org/abs/2409.03183
[18] H. Q. Ngo, M. Guo, and H. Nguyen, “Catch Me if You Can: Effective Honeypot Placement in Dynamic AD Attack Graphs,” 2023. [Online]. Available: https://arxiv.org/abs/2312.16820
[19] Irfan Murti Raazi, Ima Dwitawati, and Putri Nabila, “Uji Vulnerability Assessment Dalam Mengetahui Tingkat Keamanan Web Aplikasi Sistem Informasi Laporan Diskominfo Dan Sandi Aceh,” J-INTECH: Journal Of Information Technology, vol. 4, no. 1, pp. 1–15, Feb. 2023, doi: 10.22373/j-intech.v4i1.2409.
[20] A. Ebunoluwa and A. James, “AI-Powered Honeypots: Enhancing Deception Technologies for Cyber Defense,” Jun. 2025.
[21] M. B. -, “AI-enhanced Honeypots for Zero-Day Exploit Detection and Mitigation,” International Journal For Multidisciplinary Research, vol. 6, no. 6, Dec. 2024, doi: 10.36948/ijfmr.2024.v06i06.32866.
[22] Z. Zhang et al., “Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space,” 2025. [Online]. Available: https://arxiv.org/abs/2505.15778