Hybrid Deep Learning and Rule-Based Models for Real-Time Intrusion Detection in IoT Networks: Extending IDS to Edge AI

References

[1] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

[2] Raymaekers, J., Verbeke, W., & Verdonck, T. (2021). Weight-of-evidence 2.0 with shrinkage and spline-binning. arXiv preprint arXiv:2101.01494. Retrieved from https://arxiv.org/abs/2101.01494

[3] Jain, M., & Srihari, A. (2024). Comparison of Machine Learning Algorithm in Intrusion Detection Systems: A Review Using Binary Logistic Regression. International Journal of Computer Science and Mobile Computing, Vol.13 Issue.10, October- 2024, pg. 45-53

[4] Kaushik, P., Jain, M., & Shah, A. (2018). A Low Power Low Voltage CMOS Based Operational Transconductance Amplifier for Biomedical Application. https://ijsetr.com/uploads/136245IJSETR17012-283.pdf

[5] West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection: A comprehensive review. Computers & Security, 57, 47–66. https://doi.org/10.1016/j.cose.2015.09.005

[6] Kaushik, P.; Jain, M.: Design of low power CMOS low pass filter for biomedical application. J. Electr. Eng. Technol. (IJEET) 9(5) (2018)

[7] Bauer, S., Wiest, R., Nolte, L. P., & Reyes, M. (2013). A survey of MRI-based medical image analysis for brain tumour studies. Physics in Medicine & Biology, 58(13), R97–R129. https://doi.org/10.1088/0031-9155/58/13/R97

[8] Kaushik, P., & Jain, M. A Low Power SRAM Cell for High Speed Applications Using 90nm Technology. Csjournals. Com, 10. https://www.csjournals.com/IJEE/PDF10- 2/66.%20Puneet.pdf

[9] Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016). Performance measures and a data set for multi-target, multi-camera tracking. In Proceedings of the European Conference on Computer Vision Workshops (ECCVW).

[10] Puneet Kaushik, Mohit Jain. ―A Low Power SRAM Cell for High Speed Applications Using 90nm Technology.‖ Csjournals.Com 10, no. 2 (December 2018): 6.https://www.csjournals.com/IJEE/PDF10- 2/66.%20Puneet.pdf

[11] Mohit Jain, Adit Shah (2024). Anomaly Detection Using Convolutional Neural Networks (CNN). ESP International Journal of Advancements in Computational Technology. https://www.espjournals.org/IJACT/2024/Volume2-Issue3/IJACT-V2I3P102.pdf

[12] Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2017). Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3784–3797. https://doi.org/10.1109/TNNLS.2017.2736643

[13] Puneet Kaushik, Mohit Jain, Aman Jain, “A Pixel-Based Digital Medical Images Protection Using Genetic Algorithm,” International Journal of Electronics and Communication Engineering, ISSN 0974-2166 Volume 11, Number 1, pp. 31-37, (2018).

[14] Charron, O., Lallement, A., Jarnet, D., Noblet, V., Clavier, J. B., & Meyer, P. (2018). Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Computers in Biology and Medicine, 95, 43–54. https://doi.org/10.1016/j.compbiomed.2018.02.004

[15] Kaushik, P., Jain, M., & Shah, A. (2018). A Low Power Low Voltage CMOS Based Operational Transconductance Amplifier for Biomedical Application.

[16] Jain, M., & Arjun Srihari. (2024b). Comparison of Machine Learning Models for Stress Detection from Sensor Data Using Long Short-Term Memory (LSTM) Networks and Convolutional Neural Networks (CNNs). International Journal of Scientific Research and Management (IJSRM), 12(12), 1775–1792. https://doi.org/10.18535/ijsrm/v12i12.ec02

[17] Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017). Brain tumour segmentation with deep neural networks. Medical Image Analysis, 35, 18–31. https://doi.org/10.1016/j.media.2016.05.004

[18] Mohit Jain, Arjun Srihari (2024). Comparison of Machine Learning Models for Stress Detection from Sensor Data Using Long Short-Term Memory (LSTM) Networks and Convolutional Neural Networks (CNNs). https://ijsrm.net/index.php/ijsrm/article/view/5912/3680 https://doi.org/10.18535/ijsrm/v12i12.ec02

[19] InsiderFinance Wire. (2021). Logistic regression: A simple powerhouse in fraud detection. Medium. Retrieved from https://wire.insiderfinance.io/logistic-regression-a-simple-powerhouse-in-fraud-detection-15ab984b2102

[20] Puneet Kaushik, Mohit Jain. ―A Low Power SRAM Cell for High Speed ApplicationsUsing 90nm Technology.‖ Csjournals.Com 10, no. 2 (December 2018): 6.https://www.csjournals.com/IJEE/PDF10-2/66.%20Puneet.pdf

[21] Jain, M., & Arjun Srihari. (2024). Comparison of CAD Detection of Mammogram with SVM and CNN. Iconic Research and Engineering Journals, 8(6), 63–75. https://www.irejournals.com/paper-details/1706647

[22] Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018). Artificial intelligence in radiology. Nature Reviews Cancer, 18(8), 500–510. https://doi.org/10.1038/s41568-018-0016-5

[23] Jain, M., & None Arjun Srihari. (2023). House price prediction with Convolutional Neural Network (CNN). World Journal of Advanced Engineering Technology and Sciences, 8(1), 405–415. https://doi.org/10.30574/wjaets.2023.8.1.0048

[24] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539

[25] Jain, M., & Shah, A. (2022). Machine Learning with Convolutional Neural Networks (CNNs) in Seismology for Earthquake Prediction. Iconic Research and Engineering Journals, 5(8), 389–398. https://www.irejournals.com/paper-details/1707057

[26] Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005

[27] Jain, M., & Srihari, A. (2021). Comparison of CAD detection of mammogram with SVM and CNN. IRE Journals, 8(6), 63-75. https://www.irejournals.com/formatedpaper/1706647.pdf

[28] Bhat, N. (2019). Fraud detection: Feature selection-over sampling. Kaggle. Retrieved from https://www.kaggle.com/code/nareshbhat/fraud-detection-feature-selection-over-sampling

[29] Mohit Jain and Arjun Srihari (2023). House price prediction with Convolutional Neural Network (CNN). https://wjaets.com/sites/default/files/WJAETS-2023-0048.pdf

[30] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (NIPS).

[31] Kayalibay, Baris, et al. “CNN-Based Segmentation of Medical Imaging Data.” ArXiv:1701.03056 [Cs], 25 July 2017, arxiv.org/abs/1701.03056.

[32] Shorten, Connor, and Taghi M. Khoshgoftaar. “A Survey on Image Data Augmentation for Deep Learning.” Journal of Big Data, vol. 6, no. 1, 6 July 2019, journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0197-0, https://doi.org/10.1186/s40537-019-0197-0.

[33] L. Wang, W. Chen, W. Yang, F. Bi and F. R. Yu, "A State-of-the-Art Review on Image Synthesis With Generative Adversarial Networks," in IEEE Access, vol. 8, pp. 63514-63537, 2020, doi: 10.1109/ACCESS.2020.2982224.

[34] Kaushik, P., & Jain, M. A Low Power SRAM Cell for High Speed Applications Using 90nm Technology. Csjournals. Com, 10. https://www.csjournals.com/IJEE/PDF10-2/66.%20Puneet.pdf

[35] K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/j.gltp.2022.04.020.

[36] L. Jen and Y.-H. Lin, “A Brief Overview of the Accuracy of Classification Algorithms for Data Prediction in Machine Learning Applications,” Journal of Applied Data Sciences, vol. 2, no. 3, pp. 84–92, 2021, doi: 10.47738/jads.v2i3.38.

[37] Kaushik P, Jain M, Jain A (2018) A pixel-based digital medical images protection using genetic algorithm. Int J Electron Commun Eng 11:31–37

[38] Mohit Jain and Adit Shah (2021). Convolutional neural networks for real-time object detection with raspberry Pi. https://wjaets.com/sites/default/files/WJAETS-2021-0067.pdf. https://doi.org/10.30574/wjaets.2021.4.1.0067

[39] Louis, D. N., Perry, A., Reifenberger, G., von Deimling, A., Figarella-Branger, D., Cavenee, W. K., Ohgaki, H., Wiestler, O. D., Kleihues, P., & Ellison, D. W. (2016). The 2016 World Health Organization classification of tumours of the central nervous system: A summary. Acta Neuropathologica, 131(6), 803–820. https://doi.org/10.1007/s00401-016-1545-1

[40] Jain, M., & Shah, A. (2020). A multi-modal CNN framework for integrating medical imaging for COVID-19 Diagnosis. World Journal of Advanced Research and Reviews, 8(3), 475–493. https://doi.org/10.30574/wjarr.2020.8.3.0418

[41] S. A. Hicks et al., “On evaluation metrics for medical applications of artificial intelligence,” Sci Rep, vol. 12, no. 1, pp. 1–9, Dec. 2022, doi: 10.1038/s41598-022-09954-8.

[42] Pallud, J., Fontaine, D., Duffau, H., Mandonnet, E., Sanai, N., Taillandier, L., Peruzzi, P., Guillevin, R., Bauchet, L., Bernier, V., Baron, M.-H., Guyotat, J., & Capelle, L. (2010). Natural history of incidental World Health Organization grade II gliomas. Annals of Neurology, 68(5), 727–733. https://doi.org/10.1002/ana.22106

[43] Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016). Brain tumour segmentation using convolutional neural networks in MRI images. IEEE Transactions on Medical Imaging, 35(5), 1240–1251. https://doi.org/10.1109/TMI.2016.2538465

[44] Kaushik, P. (2018). STUDY AND ANALYSIS OF IMAGE ENCRYPTION ALGORITHM BASED ON ARNOLD TRANSFORMATION. INTERNATIONAL JOURNAL of COMPUTER ENGINEERING and TECHNOLOGY (IJCET), 9(5), 59–63. https://iaeme.com/Home/article_id/IJCET_09_05_008

[45] Patel, H., & Zaveri, M. (2011). Credit card fraud detection using neural network. International Journal of Innovative Research in Computer and Communication Engineering, 1(2), 1–6. https://www.ijircce.com/upload/2011/october/1_Credit.pdf

[46] Kaushik, P., & Jain, M. (2018). Design of low power CMOS low pass filter for biomedical application. International Journal of Electrical Engineering & Technology (IJEET), 9(5).

[47] Alom, Md Zahangir, et al. “The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.” ArXiv:1803.01164 [Cs], 12 Sept. 2018, arxiv.org/abs/1803.01164.

[48] Wang, Weibin, et al. “Medical Image Classification Using Deep Learning.” Intelligent Systems Reference Library, 19 Nov. 2019, pp. 33–51, https://doi.org/10.1007/978-3-030-32606-7_3.

[49] Nabati, R., & Qi, H. (2019). "RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles." 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 2019, pp. 3093-3097, doi: 10.1109/ICIP.2019.8803392.

[50] Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (pp. 234–241). Springer. https://doi.org/10.1007/978-3-319-24574-4_28

[51] Mohit Jain | Puneet Kaushik | Adit Shah "Comparison of VGG16 and VGG19 Convolutional Neural Network (CNN) Layers on MRI Brain Tumor Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-1, December 2016, pp.275-280, URL: https://www.ijtsrd.com/papers/ijtsrd3542.pdf

@article{1711965,
      author = {Rajat Paswan, Atishay Prem, Nitin Jain},
      title = {Hybrid Deep Learning and Rule-Based Models for Real-Time Intrusion Detection in IoT Networks: Extending IDS to Edge AI},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {3},
      pages = {2085-2090},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711965.pdf},
      abstract = {The rapid expansion of Internet of Things (IoT) networks has introduced significant security vulnerabilities, necessitating intelligent Intrusion Detection Systems (IDS) capable of operating under constrained edge environments. This paper presents a hybrid framework combining deep learning and rule-based models for real-time intrusion detection in IoT ecosystems. The proposed Edge-IDS integrates a CNN-LSTM-based deep model for behavioral pattern extraction with Snort-inspired rule-based decision fusion for anomaly validation. Evaluation across BoT-IoT, TON-IoT, and CICIDS2019 datasets demonstrates an average detection accuracy of 98.6% and latency reduction of 31% compared to centralized IDS architectures. The framework?s edge-deployable nature and adaptability to dynamic IoT environments make it suitable for future 6G and industrial automation networks.},
      month = {September},
      doi = {https://doi.org/10.64388/IREV9I4-1711286}
  }