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Original Article
AI-Powered Real-Time CNN-LSTM Intrusion Detection: From Streaming Traffic to Actionable Alerts
Kalyana Krishna Kondapalli1
1 Technical Architect, IT Platform Senior Cloud Engineer, Andhra Pradesh, India.
Published Online: March-April 2026
Pages: 123-128
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20260602018
References
1. Y. Zhang, R. C. Muniyandi, and F. Qamar, "A Review of Deep Learning Applications in Intrusion Detection Systems: Overcoming Challenges in Spatiotemporal Feature Extraction and Data Imbalance," Applied Sciences, vol. 15, no. 3, Art. no. 1552, Feb. 2025, doi: 10.3390/app15031552.
2. A. Hozouri, A. Mirzaei, and M. Effatparvar, "A comprehensive survey on intrusion detection systems with advances in machine learning, deep learning and emerging cybersecurity challenges," Discover Artificial Intelligence, vol. 5, no. 1, Art. no. 314, Nov. 2025, doi: 10.1007/s44163-025-00578-1.
3. U. Ahmed, M. Nazir, A. Sarwar, T. Ali, E.-H. M. Aggoune, T. Shahzad, and M. A. Khan, "Signature-based intrusion detection using machine learning and deep learning approaches empowered with fuzzy clustering," Scientific Reports, vol. 15, Art. no. 1726, Jan. 2025, doi: 10.1038/s41598-025-85866-7.
4. P. Sinha, D. Sahu, S. Prakash, R. Singh Rathore, et al., "A high performance hybrid LSTM-CNN secure architecture for IoT environments using deep learning," Scientific Reports, vol. 15, Art. no. 9684, Mar. 2025, doi: 10.1038/s41598-025-94500-5.
5. F. Parveen, S. Iqbal, G. Mumtaz, and M. Salahuddin, "Real-Time Intrusion Detection with Deep Learning: Analyzing the UNR Intrusion Detection Dataset," Journal of Computing & Biomedical Informatics, vol. 7, no. 02, Sep. 2024, doi: 10.56979/702/2024.
6. N. Samout, T. Gouasmia, and N. Nasri, "Explainable AI-based innovative models for intrusion detection in Big Data WSN," Procedia Computer Science, vol. 270, pp. 5065–5072, Nov. 2025, doi: 10.1016/j.procs.2025.09.633.
7. P. Dey and D. Bhakta, "A new Random Forest and Support Vector Machine-based intrusion detection model in networks," National Academy Science Letters, vol. 46, pp. 471–477, Feb. 2023, doi: 10.1007/s40009-023-01223-0.
8. Y. Mo, H. Li, D. Wang, and G. Liu, "An intrusion detection system based on convolution neural network," PeerJ Computer Science, vol. 10, p. e2152, Jun. 2024, doi: 10.7717/peerj-cs.2152.
9. Canadian Institute for Cybersecurity, "CIC-IDS2017: Intrusion Detection Evaluation Dataset," University of New Brunswick, 2017.
10. P. Waghmode, M. Kanumuri, H. El-Ocla, and T. Boyle, "Intrusion detection system based on machine learning using least square support vector machine," Scientific Reports, vol. 15, Art. no. 12066, Apr. 2025, doi: 10.1038/s41598-025-95621-7.
11. V. Kadam and R. Verma, "Evaluating Effectiveness: A Critical Review of Performance Metrics in Intrusion Detection System," Journal of Engineering Science and Technology Review, vol. 18, no. 1, pp. 199–209, 2025, doi: 10.25103/jestr.181.20.
12. K. K. Kondapalli and C. Gunupudi, "A Hybrid Zero-Trust-Driven Cloud Architecture for Securing Distributed Electronic Health Records in AI-Enabled Healthcare Ecosystems," Int. J. Adv. Res. Eng. Technol. (IJARET), vol. 9, no. 2, pp. 116–131, Mar.–Apr. 2018, doi: 10.34218/IJARET_09_02_015.
13. K. K. Kondapalli and C. Gunupudi, "Artificial Intelligence Ethics and Principles in Public Sector Healthcare: A Governance Framework for Responsible AI Adoption in Local and Sub-National Health Services," Lex Localis – J. Local Self-Gov., vol. 21, no. S2, pp. 61–81, Sep. 2023, doi: 10.52152/jvt38g16.
14. H. C. Altunay and Z. Albayrak, "A hybrid CNN+LSTM-based intrusion detection system for industrial IoT networks," Engineering Science and Technology, an International Journal, vol. 38, Art. no. 101322, Feb. 2023, doi: 10.1016/j.jestch.2022.101322.
15. A. K. Pandey, A. K. Mishra, R. K. Tripathi, A. K. Jain, and N. Kumar, "Towards a unified intrusion detection system using machine learning on NSL-KDD dataset," Expert Systems with Applications, vol. 185, Art. no. 115576, Dec. 2021, doi: 10.1016/j.eswa.2021.115576.
16. A. Diro and N. Chilamkurti, "Distributed attack detection scheme using deep learning approach for Internet of Things," Future Generation Computer Systems, vol. 82, pp. 761–768, May 2018, doi: 10.1016/j.future.2017.08.077.
17. M. A. Ferrag, L. Maglaras, A. Ahmim, M. Derdour, and H. Janicke, "RDTIDS: Rules and decision tree-based intrusion detection system for Internet-of-Things networks," Future Internet, vol. 12, no. 3, Art. no. 44, Mar. 2020, doi: 10.3390/fi12030044.
18. Kalyana Krishna Kondapalli. Transparent AI for Student Retention: An Integrated Prediction, Explanation, and Intervention System. International Journal of Artificial Intelligence & Machine Learning (IJAIML), 1(2), 2020, pp. 1-20.
19. Nikhil Kassetty, Kalyana Krishna Kondapalli. (2021). Real-Time Fraud Detection and Anomaly Monitoring in High-Volume Payment Transaction Networks. ISCSITR- International Journal of Computer Applications (ISCSITR-IJCA), 2(1), 8–22.
20. K. K. Kondapalli, K. N. Srinivasan, S. Venkatasubramanian, G. Rayapati, R. Kohli and C. Gunupudi, "AI-Driven Secure Bed Allocation Ecosystem for Multi-Hospital Networks," 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN), Lonawala,Maharashtra, India, 2025, pp. 1-5, doi: 10.1109/GCWCN66157.2025.11448383.
21. Bhanupriya Singh (2025) Artificial Intelligence in Neurology: A Research Study on Machine Learning Applications in Brain Imaging, Diagnosis, and Prognosis, Journal of Carcinogenesis, Vol.24, No.9s, 119-126.
22. Kondapalli KK, Gunupudi C. Real-Time Explainable Multimodal ML for Clinical Decision Intelligence A Hybrid Supervised–Unsupervised CDSS Framework. Int J Drug Deliv Technol. 2026;16(16s): 803-813. DOI: 10.25258/ijddt.16.16s.87.
2. A. Hozouri, A. Mirzaei, and M. Effatparvar, "A comprehensive survey on intrusion detection systems with advances in machine learning, deep learning and emerging cybersecurity challenges," Discover Artificial Intelligence, vol. 5, no. 1, Art. no. 314, Nov. 2025, doi: 10.1007/s44163-025-00578-1.
3. U. Ahmed, M. Nazir, A. Sarwar, T. Ali, E.-H. M. Aggoune, T. Shahzad, and M. A. Khan, "Signature-based intrusion detection using machine learning and deep learning approaches empowered with fuzzy clustering," Scientific Reports, vol. 15, Art. no. 1726, Jan. 2025, doi: 10.1038/s41598-025-85866-7.
4. P. Sinha, D. Sahu, S. Prakash, R. Singh Rathore, et al., "A high performance hybrid LSTM-CNN secure architecture for IoT environments using deep learning," Scientific Reports, vol. 15, Art. no. 9684, Mar. 2025, doi: 10.1038/s41598-025-94500-5.
5. F. Parveen, S. Iqbal, G. Mumtaz, and M. Salahuddin, "Real-Time Intrusion Detection with Deep Learning: Analyzing the UNR Intrusion Detection Dataset," Journal of Computing & Biomedical Informatics, vol. 7, no. 02, Sep. 2024, doi: 10.56979/702/2024.
6. N. Samout, T. Gouasmia, and N. Nasri, "Explainable AI-based innovative models for intrusion detection in Big Data WSN," Procedia Computer Science, vol. 270, pp. 5065–5072, Nov. 2025, doi: 10.1016/j.procs.2025.09.633.
7. P. Dey and D. Bhakta, "A new Random Forest and Support Vector Machine-based intrusion detection model in networks," National Academy Science Letters, vol. 46, pp. 471–477, Feb. 2023, doi: 10.1007/s40009-023-01223-0.
8. Y. Mo, H. Li, D. Wang, and G. Liu, "An intrusion detection system based on convolution neural network," PeerJ Computer Science, vol. 10, p. e2152, Jun. 2024, doi: 10.7717/peerj-cs.2152.
9. Canadian Institute for Cybersecurity, "CIC-IDS2017: Intrusion Detection Evaluation Dataset," University of New Brunswick, 2017.
10. P. Waghmode, M. Kanumuri, H. El-Ocla, and T. Boyle, "Intrusion detection system based on machine learning using least square support vector machine," Scientific Reports, vol. 15, Art. no. 12066, Apr. 2025, doi: 10.1038/s41598-025-95621-7.
11. V. Kadam and R. Verma, "Evaluating Effectiveness: A Critical Review of Performance Metrics in Intrusion Detection System," Journal of Engineering Science and Technology Review, vol. 18, no. 1, pp. 199–209, 2025, doi: 10.25103/jestr.181.20.
12. K. K. Kondapalli and C. Gunupudi, "A Hybrid Zero-Trust-Driven Cloud Architecture for Securing Distributed Electronic Health Records in AI-Enabled Healthcare Ecosystems," Int. J. Adv. Res. Eng. Technol. (IJARET), vol. 9, no. 2, pp. 116–131, Mar.–Apr. 2018, doi: 10.34218/IJARET_09_02_015.
13. K. K. Kondapalli and C. Gunupudi, "Artificial Intelligence Ethics and Principles in Public Sector Healthcare: A Governance Framework for Responsible AI Adoption in Local and Sub-National Health Services," Lex Localis – J. Local Self-Gov., vol. 21, no. S2, pp. 61–81, Sep. 2023, doi: 10.52152/jvt38g16.
14. H. C. Altunay and Z. Albayrak, "A hybrid CNN+LSTM-based intrusion detection system for industrial IoT networks," Engineering Science and Technology, an International Journal, vol. 38, Art. no. 101322, Feb. 2023, doi: 10.1016/j.jestch.2022.101322.
15. A. K. Pandey, A. K. Mishra, R. K. Tripathi, A. K. Jain, and N. Kumar, "Towards a unified intrusion detection system using machine learning on NSL-KDD dataset," Expert Systems with Applications, vol. 185, Art. no. 115576, Dec. 2021, doi: 10.1016/j.eswa.2021.115576.
16. A. Diro and N. Chilamkurti, "Distributed attack detection scheme using deep learning approach for Internet of Things," Future Generation Computer Systems, vol. 82, pp. 761–768, May 2018, doi: 10.1016/j.future.2017.08.077.
17. M. A. Ferrag, L. Maglaras, A. Ahmim, M. Derdour, and H. Janicke, "RDTIDS: Rules and decision tree-based intrusion detection system for Internet-of-Things networks," Future Internet, vol. 12, no. 3, Art. no. 44, Mar. 2020, doi: 10.3390/fi12030044.
18. Kalyana Krishna Kondapalli. Transparent AI for Student Retention: An Integrated Prediction, Explanation, and Intervention System. International Journal of Artificial Intelligence & Machine Learning (IJAIML), 1(2), 2020, pp. 1-20.
19. Nikhil Kassetty, Kalyana Krishna Kondapalli. (2021). Real-Time Fraud Detection and Anomaly Monitoring in High-Volume Payment Transaction Networks. ISCSITR- International Journal of Computer Applications (ISCSITR-IJCA), 2(1), 8–22.
20. K. K. Kondapalli, K. N. Srinivasan, S. Venkatasubramanian, G. Rayapati, R. Kohli and C. Gunupudi, "AI-Driven Secure Bed Allocation Ecosystem for Multi-Hospital Networks," 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN), Lonawala,Maharashtra, India, 2025, pp. 1-5, doi: 10.1109/GCWCN66157.2025.11448383.
21. Bhanupriya Singh (2025) Artificial Intelligence in Neurology: A Research Study on Machine Learning Applications in Brain Imaging, Diagnosis, and Prognosis, Journal of Carcinogenesis, Vol.24, No.9s, 119-126.
22. Kondapalli KK, Gunupudi C. Real-Time Explainable Multimodal ML for Clinical Decision Intelligence A Hybrid Supervised–Unsupervised CDSS Framework. Int J Drug Deliv Technol. 2026;16(16s): 803-813. DOI: 10.25258/ijddt.16.16s.87.
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