Intelligent IoT-Enabled Deep Learning System for Advanced Cybersecurity Anomaly Detection
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I2P133Keywords:
IoT Security, Cybersecurity, Anomaly Detection, Deep Learning, CNN, BiLSTM, Intrusion Detection System, UNSW-NB15, SMOTE, Network SecurityAbstract
The Internet of Things (IoT) is rapidly increasing attack surface of cyber threats, making the existing security measures insufficient to detect complex and dynamic abnormalities. The article presents a plan for a sophisticated system that uses deep learning (DL) and the internet of things (IoT) to identify cybersecurity irregularities using a CNN-BiLSTM architecture. Convolutional Neural Networks (CNNs) easily extract geographic information, whereas Bidirectional Long Short-Term Memory (BiLSTM) networks detect temporal correlations in network data. After label encoding, normalization, and SMOTE-based data balancing, the proposed model is evaluated on the UNSW-NB15 dataset, thereby increasing its consistency. As shown in experiments, the suggested hybrid model has a higher performance rate as it reaches 99.3% testing accuracy (acc), high precisions (prec), recall (rec), and a low FNR, which is significantly better than the traditional machine learning (ML) and standalone models of DL. The system successfully identifies both known and undiscovered cyber threats, making it ideal for real-time IoT applications. This study promotes the idea of scalable, accurate, and intelligent IDS to safeguard modern IoT networks.
References
[1] B. N. I. S. Phalke and Y. D. Athave, “A Multi-Layered Approach to IT Infrastructure Governance and Compliance: Security, Hardening, and Audit Readiness,” Int. J. Comput. Appl., vol. 187, no. 12, p. 9, 2025.
[2] B. F. More and S. Pawar, “Smart Home System Using Iot,” Int. J. Manag. Technol. Eng., vol. 8, no. XI, p. 2241, 2018.
[3] S. H. A. Pushkala, V. K. Jammula, A. Chowdhury, D. Reboredo, and A. Shehata, “Method and system to predict network performance of a fixed wireless network,” US12114185B2, Oct. 2024
[4] R. rao Thallada and N. Alapati, “Privacy and Cybersecurity Convergence: GRC Controls for Data Protection,” J. Bus. Manag. Stud., vol. 8, no. 5, pp. 42–48, March, 2026, doi: 10.32996/jbms.
[5] S. Priyadarshini, C. Althati, M. Tomar, K. R. Jinna, T. Pichaimani, and V. P. Rambabu, “A Scalable Digital Twin Architecture for Intelligent Cyber Physical Systems,” in 2026 International Conference on Machine Learning and Autonomous Systems (ICMLAS), Bangkok, Thailand: IEEE, 2026, pp. 1841–1847, March. doi: 10.1109/ICMLAS67792.2026.11483673.
[6] S. K. Chintagunta, “Enhancing Cloud Database Security Through Intelligent Threat Detection and Risk Mitigation,” TIJER – Int. Res. J., vol. 9, no. 10, pp. 49–55, 2022.
[7] H. P. Cyril, “DeepNetDetect: A Deep Learning-Based Approach for Early Anomaly Detection in Network Traffic,” in 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), IEEE, Feb. 2026, pp. 1–6. doi: 10.1109/ICAIC67076.2026.11395734.
[8] D. Bhattacharjee, “Design and Evaluation of Deep Generative AI Model for Intrusion Detection in Cyber Threat Monitoring,” in 2025 7th International Symposium on Advanced Electrical and Communication Technologies (ISAECT), Mohali, Punjab, India: IEEE, 2025, pp. 1–6, December. doi: https://doi.org/10.1109/ISAECT68904.2025.11318752.
[9] V. Pal and S. Amrale, “Quantum-Resistant Federated Learning Framework for Secure IoT Networks with Lattice-Based Cryptography,” in 2025 Seventh International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), IEEE, Dec. 2025, pp. 343–350. doi: 10.1109/ICRCICN68210.2025.11364899.
[10] M. Kumar and M. K. Shah, “AI-Driven DDoS Detection for Network Security: A Performance Analysis of Machine-Deep Learning Methods on Network Traffic Data,” in 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), Houston, TX, USA: IEEE, 2026, pp. 1–6, February. doi: 10.1109/ICAIC67076.2026.11395710.
[11] Naveen Kolli, John Wesly Sajja, Anusha Nerella, “Building Secure AI Agents for Autonomous Data Access in Compliance/Regulatory-Critical Environments,” Comput. Fraud Secur., pp. 363–373, 2024, doi: 10.52710/cfs.746.
[12] V. K. Sharma, “AI-Based Anomaly Detection for 5G Core and RAN Components,” Int. J. Sci. Res. Eng. Manag., vol. 6, no. 1, pp. 1–6, june, 2022, doi: 10.55041/IJSREM11453.
[13] R. Palwe, “Three Layers of Trust in AI Interfaces: Interface, Behavior, and Organization,” Int. J. Sci. Res., vol. 15, no. 1, pp. 1152–1160, Jan. 2026, doi: 10.21275/SR26112072531.
[14] R. Dattangire, R. Vaidya, D. Biradar, and A. Joon, “Exploring the Tangible Impact of Artificial Intelligence and Machine Learning: Bridging the Gap between Hype and Reality,” in 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET), 2024, pp. 1–6. doi: 10.1109/ACET61898.2024.10730334.
[15] K. K. Mohammed, “Leadership Practices of Data Engineering for AI and Machine Learning,” Int. J. Sci. Res. Eng. Trends, vol. 12, no. 1, pp. 1–5, 2026.
[16] P. Nutalapati, J. R. Vummadi, S. Dodda, and N. Kamuni, “Advancing Network Intrusion Detection: A Comparative Study of Clustering and Classification on NSL-KDD Data,” in 2025 International Conference on Data Science and Its Applications (ICoDSA), Jakarta, Indonesia: IEEE, 2025, pp. 880–885, july. doi: 10.1109/ICoDSA67155.2025.11157595.
[17] A. R. Toorpu, S. K. Vududala, A. Nerella, and B. P. Madupati, “Hybrid AI Models for Privacy-Preserving Big Data Analytics in Distributed Environments,” in 2025 Global Conference in Emerging Technology (GINOTECH), PUNE, India: IEEE, 2025, pp. 1–8, July. doi: 10.1109/GINOTECH63460.2025.11076666.
[18] S. Sudheer, “Data Engineering for Dynamic and Secure Blockchain Networks in AI Applications,” Int. J. Inf. Electron. Eng., vol. 13, no. 4, pp. 52–61, May, 2025, [Online]. Available: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=kKQadycAAAAJ&citation_for_view=kKQadycAAAAJ:SeFeTyx0c_EC
[19] S. Manekar, “An Intelligent Deep Learning Framework for Anomaly Detection in IoT Cybersecurity,” J. Artif. Intell. Healthc. FinTech Syst., vol. 1, no. 1, pp. 23–29, 2026.
[20] M. P. K, D. Marotka, M. J, R. Adhvaryu, V. V, and R. Maranan, “Traffic Surveillance System Through Capsule Gated Graph Attention Network for IoT/IIoT Cyberthreat Detection and Mitigation,” in 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS), IEEE, Mar. 2025, pp. 1974–1980. doi: 10.1109/ICMLAS64557.2025.10968575.
[21] S. Aslam, M. M. R. Alshoweky, and M. Saad, “Binary and Multiclass Classification of Attacks in Edge IIoT Networks,” in 2024 Advances in Science and Engineering Technology International Conferences (ASET), IEEE, Jun. 2024, pp. 01–05. doi: 10.1109/ASET60340.2024.10708745.
[22] X. Li, C. Xie, Z. Zhao, C. Wang, and H. Yu, “Anomaly Detection Algorithm of Industrial Internet of Things Data Platform Based on Deep Learning,” IEEE Trans. Green Commun. Netw., vol. 8, no. 3, pp. 1037–1048, Sep. 2024, doi: 10.1109/TGCN.2024.3403102.
[23] S. Chakraborty, S. K. Pandey, S. Maity, and L. Dey, “Detection and Classification of Novel Attacks and Anomaly in IoT Network using Rule based Deep Learning Model,” SN Comput. Sci., vol. 5, no. 8, p. 1056, Nov. 2024, doi: 10.1007/s42979-024-03429-5.
[24] L. Zhao, B. H. Li, J. Jia, and T. Wu, “Anomaly Detection Technology for Cloud Manufacturing System based on Data Denoising and Feature Optimization,” in ICNSC 2022 - Proceedings of 2022 IEEE International Conference on Networking, Sensing and Control: Autonomous Intelligent Systems, 2022. doi: 10.1109/ICNSC55942.2022.10004139.
[25] D. Jain and S. Jain, “Artificial Intelligence (AI)-Driven Network Traffic Anomaly Detection for IT Infrastructure Security,” in 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), IEEE, Feb. 2026, pp. 1–6. doi: 10.1109/ICAIC67076.2026.11395685.
[26] H. Nandanwar and R. Katarya, “TL-BILSTM IoT: transfer learning model for prediction of intrusion detection system in IoT environment,” Int. J. Inf. Secur., vol. 23, no. 2, pp. 1251–1277, 2024.
[27] B. Madupati, M. M. Mohammed, L. Upadhyay, D. P. Guda, K. Kaushik, and M. Soni, “Integrating Artificial Intelligence with Cybersecurity for Resilient Wireless Communication Against Advanced Threats,” in 2025 International Conference on Artificial Intelligence and Machine Vision (AIMV), IEEE, Aug. 2025, pp. 1–5. doi: 10.1109/AIMV66517.2025.11203666.
[28] A. Javed, A. Ehtsham, M. Jawad, M. N. Awais, A.-H. Qureshi, and H. Larijani, “Implementation of Lightweight Machine Learning-Based Intrusion Detection System on IoT Devices of Smart Homes,” Futur. Internet, vol. 16, no. 6, p. 200, Jun. 2024, doi: 10.3390/fi16060200.
[29] M. Gopalswamy, “Building Scalable Anomaly Identification Systems to IoT Threat Mitigation using Machine learning Techniques,” J. Glob. Res. Math. Arch., vol. 12, no. 1, 2025, doi: 10.5281/zenodo.15201777.
[30] N. Sarwar, I. S. Bajwa, M. Z. Hussain, M. Ibrahim, and K. Saleem, “IoT Network Anomaly Detection in Smart Homes Using Machine Learning,” IEEE Access, vol. 11, pp. 119462–119480, 2023, doi: 10.1109/ACCESS.2023.3325929.
[31] S. M. Kasongo and Y. Sun, “Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset,” J. Big Data, vol. 7, no. 1, 2020, doi: 10.1186/s40537-020-00379-6.
[32] F. Lawrence and R. Nigam, “High-Accuracy Intrusion Detection System using Deep Learning Ensembles and Reinforcement Learning on the NF-UNSW-NB15 Dataset,” J. Informatics Educ. Res., vol. 5, no. 4, pp. 173–199, 2025.
