Real-Time Intrusion Detection at the IoT Edge: A Hybrid Neural and Rule-Based Framework with Adaptive Fusion

References

[1] Mercado, S. T., Rasmussen, K. C., & Salcedo, C. M. (2023). A Comparative Study of Generative Adversarial Networks for Image Denoising. Pattern Recognition, 40(6), 147–153. https://doi.org/10.2925/c693143.2021.6765

[2] Vasquez, M. J., Halvorsen, V. J., & Chatterjee, W. R. “A Comparative Study of Self-Supervised Representation Learning for Fraud Detection,” Information Sciences, vol. 40, no. 4, pp. 68-85, 2018, doi: 10.9595/q895721.2020.7580.

[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] Fedorov, C. L., Ortega, P. G., Thakur, K. N., & Nascimento, M. P. “Metric Learning for Intrusion Detection in Edge Devices,” Journal of Machine Learning Research, vol. 41, no. 1, pp. 174-181, 2015, doi: 10.6764/u562169.2015.4110.

[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] Wojcik, J. B., Toledo, K. L., & Grigoriev, N. S. (2019). A Comparative Study of Metric Learning for Anomaly Detection. IEEE Transactions on Neural Networks and Learning Systems, 42(9), 386–421. https://doi.org/10.7719/f591270.2022.4951

[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] Navarro, O. F., & Kallas, M. N. (2015). Metric Learning: An Application to Object Detection. Computers in Biology and Medicine, 44(10), 73–82. https://doi.org/10.2485/n664293.2024.7300

[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] Okafor, V. V., Mercado, P. W., Leung, P. A., & Ivanov, R. M. (2022). A Comparative Study of Deep Residual Learning for Data Augmentation. IEEE Transactions on Image Processing. https://doi.org/10.2860/v164079.2019.4556

[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] Ghosh, A. P., & Radovanovic, G. V. (2024). U-Shaped Convolutional Networks: An Application to Image Segmentation. IEEE Transactions on Medical Imaging, 3(11), 79–100. https://doi.org/10.5635/w586026.2017.5091

[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] Wojcik, E. H., & Yilmaz, W. O. (2024). Federated Learning for Anomaly Detection in Medical Imaging. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.4230/u124246.2016.4657

[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] Duarte, F. P., & Berg, T. V. (2021). Attention-Based Networks for Feature Extraction in Financial Systems. Expert Systems with Applications, 8(2), 37–72. https://doi.org/10.2032/z591112.2020.6422

[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] Nakamura, D. I., & Weber, T. O. (2021). Generative Adversarial Networks: An Application to Fraud Detection. Neurocomputing. https://doi.org/10.7150/g545584.2017.9031

[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] Ortega, R. W., & Novak, T. P. (2021). Deep Residual Learning for Disease Classification in Neuroimaging. Pattern Recognition, 4(2), 17–50. https://doi.org/10.2795/j234660.2024.8549

[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] Sharabi, J. I., Wagner, C. P., & Saito, C. C. (2024). Graph Neural Networks for Motion Prediction in IoT Networks. Medical Image Analysis, 51(5), 42–81. https://doi.org/10.3589/v600363.2019.9885

[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] Duarte, H. W., Sokolov, G. O., & Zielinski, G. K. (2023). Generative Adversarial Networks: An Application to Medical Image Synthesis. Neural Networks, 68(11), 189–221. https://doi.org/10.1345/k842601.2016.4322

[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 [Crossref]

[30] Bianchi, O. T., & Nascimento, G. R. (2018). A Comparative Study of A Hybrid CNN-Transformer Model for Image Classification. Neurocomputing, 20(5), 194–203. https://doi.org/10.9585/f719740.2020.4003

[31] Tanaka, J. L., Wojcik, S. I., Vasquez, S. T., & Andersson, S. H. “Federated Learning for Scene Understanding in Healthcare Systems,” Computer Methods and Programs in Biomedicine, vol. 38, no. 10, pp. 131-148, 2023, doi: 10.7980/w104832.2018.1376.

[32] Escobar, D. G., Fischer, N. K., Mbeki, M. A., & Lindberg, W. G. (2024). Recurrent Neural Networks: An Application to Image Registration. Information Sciences, 26(8), 159–186. https://doi.org/10.3874/m610187.2022.8350

[33] Costa, G. E., Sokolov, J. F., Mahmood, L. M., & Wojcik, W. J. (2019). A Hybrid CNN-Transformer Model for Anomaly Detection in Remote Sensing. Journal of Machine Learning Research, 36(10), 15–43. https://doi.org/10.3460/z991914.2018.6285

[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 [Crossref]

[35] Nguyen, F. W., & Berg, J. N. (2016). Vision Transformers for Scene Understanding in Precision Agriculture. Neurocomputing. https://doi.org/10.5715/v572416.2023.7179

[36] Zapata, P. P., & Kowalski, D. T. (2016). Spatiotemporal Deep Networks for Scene Understanding in Remote Sensing. Computer Methods and Programs in Biomedicine. https://doi.org/10.9211/t933593.2020.3164

[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 [Crossref]

[39] Mercado, F. C., Ferreira, W. P., Radovanovic, N. I., & Thakur, I. O. (2016). Lightweight Convolutional Networks for Image Denoising in Precision Agriculture. Scientific Reports. https://doi.org/10.6852/x532754.2021.3511

[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] Suzuki, T. L., Sharabi, P. N., Fedorov, C. M., & Schneider, F. M. (2019). Deep Residual Learning for Disease Classification in Healthcare Systems. Medical Image Analysis, 15(4), 180–198. https://doi.org/10.6009/t227709.2021.8747

[42] Novak, J. D., Feng, D. R., Weber, R. W., & Sokolov, W. J. (2020). Lightweight Convolutional Networks for Intrusion Detection in Smart Manufacturing. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1766/e559859.2019.2833

[43] Leung, P. A., & Krause, L. C. (2016). Attention-Based Networks for Semantic Segmentation in Neuroimaging. Journal of Machine Learning Research, 16(8), 335–351. https://doi.org/10.4312/m924009.2015.6518

[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] Mahmood, N. N., Fischer, H. F., Radovanovic, L. V., & Zielinski, R. N. (2019). A Comparative Study of Spatiotemporal Deep Networks for Image Super-Resolution. Scientific Reports, 27(3), 120–127. https://doi.org/10.7657/f981200.2015.8116

[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] Karlsson, V. K., & Volkov, M. C. (2022). Generative Adversarial Networks: An Application to Object Detection. Computers in Biology and Medicine, 55(8), 25–59. https://doi.org/10.3136/w895088.2020.5744

[48] Halvorsen, J. M., & Toledo, E. P. (2017). Attention-Based Networks for Semantic Segmentation in Autonomous Driving. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1), 42–75. https://doi.org/10.9202/s394685.2021.8710

[49] Yamamoto, O. K., Bakshi, S. F., & Andric, G. A. (2018). A Comparative Study of Diffusion-Based Generation for Object Detection. arXiv preprint arXiv:2272.35488. https://arxiv.org/abs/2272.35488

[50] Hoffmann, H. T., Karlsson, F. A., & Garofalo, T. H. (2024). Recurrent Neural Networks: An Application to Image Registration. arXiv preprint arXiv:2282.37085. https://arxiv.org/abs/2282.37085

[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 [Crossref]

[52] Sandberg, R. H., Mbeki, A. A., & Berg, C. F. (2017). Encoder-Decoder Networks for Image Segmentation in Smart Manufacturing. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.9508/z748918.2021.6223

[53] Delgado, T. J., Radovanovic, O. P., & Wagner, P. T. (2024). Attention-Based Networks for Signal Reconstruction in Biomedical Signal Processing. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.9549/l895596.2015.8903

@article{1722545,
      author = {Beatriz Santos, Omar Farouk, Nisha Kapoor},
      title = {Real-Time Intrusion Detection at the IoT Edge: A Hybrid Neural and Rule-Based Framework with Adaptive Fusion},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {6},
      pages = {1338-1343},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722545.pdf},
      abstract = {We propose a real-time IoT intrusion detector that fuses a Transformer-LSTM neural engine with a signature rule engine through an adaptive fusion controller. Rather than fixing the balance between the two, the controller learns per-flow weights from recent reliability, trusting rules on familiar traffic and the neural model on novel patterns. On IoT-23, BoT-IoT, and CIC-IDS-2018 the system attains up to 99.4% F1 with false positives as low as 0.6%, all within a real-time latency budget.},
      month = {December},
      doi = {https://doi.org/10.64388/IREV8I6-1722545}
  }