Deep Learning–Enabled Honeypots: An ANN-Based Approach for Advanced Cyber Threat Analysis

D Nagabhushanam; U Jeevan Kumar; K Hemanth; A Navya Sai; A Partha Saradhi Reddy

doi:10.5281/zenodo.18443469

Authors

D Nagabhushanam Department of CSE (IoT, Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, A.P, India
U Jeevan Kumar Department of CSE (IoT, Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, A.P, India
K Hemanth Department of CSE (IoT, Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, A.P, India
A Navya Sai Department of CSE (IoT, Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, A.P, India
A Partha Saradhi Reddy Department of CSE (IoT, Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, A.P, India

DOI:

https://doi.org/10.5281/zenodo.18443469

Keywords:

Honeypot-Based Security, Cyber Threat Analysis, Artificial Neural Network, Deep Learning, Intrusion Detection

Abstract

– A cyber threat refers to an illegal activity intended to breach the confidentiality and integrity of computer systems and data. Examples of cyber threats include malware, phishing, unauthorized computer access, and denial-of-service attacks. It has made the traditionally relied-upon Intrusion Detection System based on signature systems obsolete and incapable of providing the much-needed protection against hacking incidents. Keeping this concern in mind, we propose an innovative concept for Deep Learning-Enabled Honeypot Cyber Attack Systems based on Honeypot-Based Artificial Neural Network Systems to facilitate efficient intelligence for effective analysis of cyber attacks. Experimental evaluation of the proposals is conducted using the CIC-IDS-2017 dataset for the attack scenario under consideration throughout the simulation. An intensive data preprocessing technique is employed to address high dimensionality, noisy features, and imbalance. The proposed HP-ANN model is systematically compared to several machine learning and deep learning baselines, namely Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). From the results, the proposed HP-ANN model significantly outperforms all baselines in terms of accuracy, precision, recall, and F1-score, achieving 1.00 and a very near-perfect ROC-AUC. Besides, the model's stability and fast learning ability are undeniable, as demonstrated by additional analyses of convergence trends and confusion matrices.

References

Al-Sayed, M., Kumar, R., & Kim, T. H. (2025). AI-driven cyberattacks and their impact on next-generation network security. IEEE Access, 13, 11245–11260.

Zhang, L., Verma, S., & Ghosh, A. (2025). Evolving threat landscapes in cloud and IoT-enabled infrastructures. IEEE Transactions on Cloud Computing, 13(1), 98–112.

Park, J., & Conti, M. (2025). Limitations of signature-based intrusion detection against advanced persistent threats. IEEE Security & Privacy, 23(2), 41–49.

Rahman, A., Ahmed, N., & Salah, K. (2025). Stealthy lateral movement detection challenges in enterprise networks. IEEE Transactions on Information Forensics and Security, 20, 1550–1564.

Alasmary, S., Zhou, Y., & Mohaisen, D. (2025). Honeypots as deception-based cybersecurity mechanisms: A comprehensive analysis. IEEE Communications Surveys & Tutorials, 27(1), 310–336.

Mishra, P., & Buyya, R. (2025). Evaluating honeypot-assisted intrusion detection systems for zero-day attack mitigation. IEEE Transactions on Network and Service Management, 22(1), 220–234.

Liu, H., Chen, F., & Wang, X. (2025). Adaptive honeypots using machine learning for intelligent cyber defense. IEEE Internet of Things Journal, 12(3), 2785–2798.

Nguyen, T., Hussain, S. R., & Bertino, E. (2025). Behavioral attack profiling using honeypot-generated telemetry and deep learning. IEEE Transactions on Dependable and Secure Computing, 22(2), 890–903.

Singh, R., & Calyam, P. (2025). Integrating honeypots with SIEM and SOAR for automated incident response. IEEE Access, 13, 54680–54695.

Hassan, M. K., Li, J., & Al-Fuqaha, A. (2025). Predictive cybersecurity analytics using deep learning on honeypot data. IEEE Transactions on Artificial Intelligence, 6(1), 120–133.

Mishra, S., & Singh, S. (2025). Artificial neural networks-based intrusion detection system for Internet of Things fog nodes. In Proceedings of the International Conference on Advanced Computing and Intelligent Technologies (pp. 1–8).

Kaur, H., Singh, G., & Kaur, J. (2025). Comparative analysis of machine learning models for cyber attack detection in network security. Standard Journal of Engineering and Technology, 136, 401–423.

Adebiyi, O., Johnson, A., & Smith, B. (2025). A hybrid machine learning framework for network anomaly detection using PCA and optimized classifiers. Scientific Electronic System, 3(5), 63–75.

Chen, L., Wang, Y., & Zhang, Z. (2025). Dual-encoder architecture with attention mechanisms for high-precision malicious traffic identification. Symmetry, 17(628), 1–15.

Suleman, A., et al. (2025). IntrusionGuard: An ANN-based approach for high-fidelity honeypot threat intelligence (Research report; Kaggle dataset implementation).

Rahman, M., Ali, S., & Hassan, K. (2025). Convolutional neural network-based botnet detection through traffic-to-image transformation. International Journal of Intelligent Information Systems, 14(4), 11–24.

Khan, F., Ahmed, R., & Doe, J. (2025). Vision transformers and cross-attention for global contextual DDoS attack identification. In Proceedings of the International Conference on AI and Information Technology (Vol. 13, No. 3, pp. 1–12).

Alhassan, A., Bello, M., & Umar, S. (2025). Robust deep neural networks for adversarial threat detection in cyber-physical systems. Procedia Computer Science, 259, 159–170.

George, A. M., Rajan, K. T., Jambula, K. R., & Ahmed, S. T. (2025, August). Adaptive Firewall System to Predict Phishing Websites using Machine Learning Model. In 2025 International Conference on Artificial Intelligence and Machine Vision (AIMV) (pp. 1-6). IEEE.

Girija, S. H., Khanum, H., Sinchana, B., Ahmed, S. T., & Rashmi, C. (2025, August). Dynamic Network Traffic Anomaly Detection Using Machine Learning. In 2025 International Conference on Artificial Intelligence and Machine Vision (AIMV) (pp. 1-6). IEEE.

Fathima, A. S., Basha, S. M., Ahmed, S. T., Khan, S. B., Asiri, F., Basheer, S., & Shukla, M. (2025). Empowering consumer healthcare through sensor-rich devices using federated learning for secure resource recommendation. IEEE Transactions on Consumer Electronics.