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Cybersecurity in the Internet of Things: the Detection of the Types of Upcoming Digital Information by Using Classification Techniques

Year 2024, Volume: 5 Issue: 2, 41 - 62
https://doi.org/10.55195/jscai.1576195

Abstract

This study addresses the critical challenge of Cyber-attacks detection (CAD) in the Internet of Things (IoT) environment, specifically focusing on the classification of non malicious and malicious network traffic. The primary objective is to enhance the accuracy and reliability of detection mechanisms through the implementation of advanced machine learning models, particularly the hybrid CNN-GRU-LSTM model. The study utilizes the SYN DoS dataset from the Kitsune Network Attack Dataset to train and evaluate various models, including Linear Discriminant Analysis (LDA), Logistic Regression, and the CNN-GRU-LSTM model. The methodology includes a comprehensive performance analysis of each model, employing metrics such as accuracy, precision, recall, and F1-score. The results reveal that both LDA and Logistic Regression achieved perfect accuracy (1.00), while the CNN-GRU-LSTM model exhibited an accuracy of 0.998. Additionally, the CNN-GRU-LSTM model demonstrated a high area under the curve (AUC) value of 0.8559, indicating strong discriminatory power. The study further employs SHAP (SHapley Additive exPlanations) for model interpretability, allowing for a detailed analysis of feature importance and insights into model behavior. In conclusion, the hybrid CNN-GRU-LSTM model offers a promising approach for effective network attack detection while providing a basis for future improvements in real-time applications and the exploration of additional datasets.

References

  • Barry, B., Chan, H. A. Barry, B., Chan, H. (2010), Intrusion Detection Systems, In: Stavroulakis, P., Stamp, M. (eds): Handbook of Information and Communication Security pp193-205, SpringerLink. DOI:10.1007/978-3-642-04117-4_10.
  • Ashiku L. and Dagli C.H. (2021). Network Intrusion Detection System using Deep Learning, Procedia Computer Science 2021, 185(1):239-247
  • Gottapu S. R. and Krishna S. P. (2023), A Novel Approach for Detection of DoS / DDoS Attack in Network Environment using Ensemble Machine Learning Model. International Journal on Recent and Innovation Trends in Computing and Communication 11(9):244-253. DOI: 10.17762/ijritcc.v11i9.8340ISBN: 2321-8169
  • Gottapu S. R. and Krishna S. P. (2024), Exploring a novel framework for DoS/DDoS attack detection and simulation in contemporary networks, January 2024i-manager’s Journal on Software Engineering 18(3):43. DOI:10.26634/jse.18.3.20596
  • Inuwa, M. M., & Das, R. (2024). A comparative analysis of various machine learning methods for anomaly detection in cyber-attacks on IoT networks. Internet of Things, 26, 101162. https://doi.org/10.1016/j.iot.2024.101162
  • Becerra-Suarez, F.L., Tuesta-Monteza, V.A., Mejia-Cabrera, H.I., Arcila-Diaz, J. (2024). Performance Evaluation of Deep Learning Models for Classifying Cybersecurity Attacks in IoT Networks. Informatics 2024, 11, 32. https://doi.org/10.3390/informatics11020032
  • Liu, H.; Lang, B. Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey. Appl. Sci. 2019, 9, 4396. https://doi.org/10.3390/app9204396
  • Amutha S., Kavitha R., Srinivasan R. and Kavitha M., "Secure network intrusion detection system using NID-RNN based Deep Learning," 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), Chennai, India, 2022, pp. 1-5, doi: 10.1109/ACCAI53970.2022.9752526.
  • Liao, H., Murah, M. Z., Hasan, M. K., Aman, A. H. M., Fang, J., Hu, X., & Khan, A. U. R. (2024). A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things. IEEE Access. vol.12, pp.4745-4761, 2024.
  • Tossou, S., Qorib, M., & Kacem, T. (2023, October). Anomaly Based Intrusion Detection System: A Deep Learning Approach. In 2023 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE. Doha, Qatar, 2023, pp. 1-6, doi: 10.1109/ISNCC58260.2023.10323740.
  • Rani, S., & Kumar, S. (2023, May). Unleashing the Power of Machine and Deep Learning for Advanced Network Intrusion Detection: An Analysis and Exploration. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) IEEE, Chennai, India, 2023, pp. 1-9, doi: 10.1109/ACCAI58221.2023.10200892.
  • Pandathara A. (2023). A Comprehensive Examination of Literature Exploring the Implementation of Machine Learning to Network Security's Intrusion Detection Systems. International Journal of Advanced Research in Science, Communication and Technology, doi: 10.48175/ijarsct-8605
  • Hussain, A., Sharif, H., Rehman, F., Kirn, H., Sadiq, A., Khan, M. S., Riaz, A., Ali, C. N., & Chandio, A. H. (2023). A systematic review of intrusion detection systems in internet of things using ML and DL. In 2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) (pp. 1-5). IEEE. https://doi.org/10.1109/iCoMET57998.2023.10099142
  • Nakip M., Gül B. C., Gelenbe E. (2023). Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection. In 2023 31st International Symposiumon Modeling, Analysis, and Simulation of Computer andTelecommunication Systems (MASCOTS). IEEE, 2023, pp. 1–8. DOI:10.1109/MASCOTS59514.2023.10387644
  • Zhu, S., Xu, X., Zhao, J., & Xiao, F. (2024). Lkd-stnn: A lightweight malicious traffic detection method for internet of things based on knowledge distillation. IEEE Internet of Things Journal, vol. 11, no. 4, pp. 6438-6453, 15 Feb.15, 2024.
  • Kheddar, H., Himeur, Y., & Awad, A. I. (2023). Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review. Journal of Network and Computer Applications, 220, 103760. https://doi.org/10.1016/j.jnca.2023.103760
  • Sunil, C. K., Reddy, S., Kanber, S. G., Sandeep, V. R., & Patil, N. (2023). Comparative analysis of intrusion detection system using ML and DL techniques. In Hybrid Intelligent Systems (pp. 736-745). Springer, Cham. https://doi.org/10.1007/978-3-031-27409-1_67
  • Mert, Nakip., Baran, Can, Gül., Erol, Gelenbe. (2023). Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection. arXiv.org, doi: 10.48550/arXiv.2306.13029
  • Wasnik P. and Chavhan N., "A Review Paper on Designing Intelligent Intrusion Detection System Using Deep Learning," 2023 11th International Conference on Emerging Trends in Engineering & Technology - Signal and Information Processing (ICETET - SIP), Nagpur, India, 2023, pp. 1-6, doi: 10.1109/ICETET-SIP58143.2023.10151563.
  • Ogundokun R. O., Basil U., Babatunde A. N., Abdulahi A. T., Adenike A. R. and Adebiyi A. A., "Intrusion Detection Systems Based on Machine Learning Approaches: A Systematic Review," 2023 International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG), Omu-Aran, Nigeria, 2023, pp. 01-04, doi: 10.1109/SEB-SDG57117.2023.10124506.
  • Krishna, A., Lal, A., Mathewkutty, A. J., Jacob, D. S., & Hari, M. (2020, July). Intrusion detection and prevention system using deep learning. In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 273-278). IEEE.
  • Fadel, M. M., El-Ghamrawy, S. M., Ali-Eldin, A. M., Hassan, M. K., & El-Desoky, A. I. (2022). HDLIDP: A Hybrid Deep Learning Intrusion Detection and Prevention Framework. Computers, Materials & Continua, 73(2).
  • Alghamdi, Mohammed I., A Hybrid Model for Intrusion Detection in IoT Applications, Wireless Communications and Mobile Computing, 2022, 4553502, 9 pages, 2022. https://doi.org/10.1155/2022/4553502
  • Monani A. Bhusnale O. Borade K. Madali R. (2023). Analysing Cyber Threats: A Comprehensive Literature Review on Data-Driven Approaches, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), Volume 9, Issue 3, pp.188-193, May-June-2023. Available at doi : https://doi.org/10.32628/CSEIT2390351
  • Auwal, Sani, Iliyasu. (2022). A Survey of Network Intrusion Detection Techniques Using Deep Learning. International Journal of Engineering Research in Computer Science and Engineering, doi: 10.36647/ijercse/09.08.art017
  • Hnamte V., Hussain J. (2023). Network Intrusion Detection using Deep Convolution Neural Network. 4th International Conference for Emerging Technology (INCET). doi: 10.1109/INCET57972.2023.10170202
  • Alabdulatif, A., & Rizvi, S.S.H. (2022). Machine learning approach for improvement in kitsune NID. Intelligent Automation & Soft Computing, 32(2), 827-840. https://doi.org/10.32604/iasc.2022.021879
  • Malliga, S., Nandhini, P. S., & Kogilavani, S. V. (2022). A comprehensive review of deep learning techniques for the detection of (distributed) denial of service attacks. Information Technology and Control, doi: 10.5755/j01.itc.51.1.29595
  • Sujatha, V., Prasanna, K. L., Niharika, K., Charishma, V., & Sai, K. B. (2023). Network intrusion detection using deep reinforcement learning. 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), 1146-1150. https://doi.org/10.1109/ICCMC56507.2023.10083673
  • Mohammed, A., Bahashwan, A.A., Manickam, S., Al-Amiedy, T.A., Aladaileh, M.A., & Hasbullah, I.H. (2023). A systematic literature review on machine learning and deep learning approaches for detecting DDoS attacks in software-defined networking. Sensors, 23(9), 4441. https://doi.org/10.3390/s23094441
  • Omarov, B., Asqar, M., Sadybekov, R., Koishiyeva, T., Bazarbayeva, A., & Uxikbayev, Y. (2022). IoT network intrusion detection: A brief review. 2022 International Conference on Smart Information Systems and Technologies (SIST), 1-5. https://doi.org/10.1109/SIST54437.2022.9945763
  • Tahreeem, M., Andleeb, I., Hussain, B. Z., & Hameed, A. (2022, December). Machine learning-based Android intrusion detection systems. Paper presented at the International Conference on Data Intensive Applications & Their Challenges (Computatia X), Jaipur, India. Aligarh Muslim University, University of Windsor, Texas A&M University.
  • Gonaygunta, H. (2023). Machine learning algorithms for detection of cyber threats using logistic regression. International Journal of Smart Sensor and Adhoc Network, 3(4), 36-42. https://doi.org/10.47893/IJSSAN.2023.1229
  • Pandey, G., Kumar, A. K., & Jha, M. (2024). Human activity recognition using CNN-LSTM-GRU model. International Research Journal on Advanced Engineering Hub (IRJAEH), 2(04), 889-894. https://doi.org/10.47392/IRJAEH.2024.012
  • Bhattarai, A., Gyawali, U., Verma, A., & Ranga, V. (2024). Improving intrusion detection in a software-defined network using hybrid CNN and Bi-LSTM. Proceedings of the 2024 IEEE International Conference on Artificial Intelligence and Computational Applications (ICAAIC). https://doi.org/10.1109/icaaic60222.2024.10575090
  • Abdulhakim, A., & Ilyas, M. (2024). Deep learning for smart grid intrusion detection: A hybrid CNN-LSTM-based model. International Journal of Artificial Intelligence & Applications (IJAIA), 15(3), 1-10. https://doi.org/10.5121/ijaia.2024.15301
  • Al-Aql, N. (2024). Hybrid RNN-LSTM networks for enhanced intrusion detection in vehicle CAN systems. Journal of Electrical Systems, 33(1), 1-8. https://doi.org/10.52783/jes.3318
  • Poornachander, V., Kumar, K. S., & Jagadish, S. (2024). DDoS attack intrusion detection system with CNN and LSTM hybridization. Proceedings of the 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS), Coimbatore, India, 1-6. https://doi.org/10.1109/ICSCSS60660.2024.10625330
  • Lv, H., & Ding, Y. (2024). A hybrid intrusion detection system with K-means and CNN+LSTM. EAI Endorsed Transactions on Scalable Information Systems, 11(6). https://doi.org/10.4108/eetsis.5667
  • Abu Khalil, D., & Abuzir, Y. (n.d.). Detecting and Analyzing Network Attacks: A Time-Series Analysis Using the Kitsune Dataset. Journal of Emerging Computer Technologies, 5(1), 9-23. https://doi.org/10.57020/ject.1563146.
  • Gür, Y. E. (2024). Comparative Analysis of Deep Learning Models for Silver Price Prediction: CNN, LSTM, GRU and Hybrid Approach. Akdeniz İİBF Dergisi, 24(1), 1-13. https://doi.org/10.25294/auiibfd.1404173
  • Scikit-learn. (2021). scikit-learn: Machine Learning in Python. Retrieved from https://scikit-learn.org/
  • Hayel, R., Hindi, K. M., Hosny, M. I., & Alharbi, R. (2024). A selective LVQ algorithm for improving instance reduction techniques and its application for text classification. Journal of Intelligent & Fuzzy Systems. https://doi.org/10.3233/JIFS-235290
  • Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning (ICML 2006).
  • Van Rijsbergen, C. J. (1979). Information Retrieval. Butterworth-Heinemann.
  • Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
  • Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS 2017), 4765-4774.
  • Lundberg, S. M., Erion, G., & Lee, S. I. (2020). Explainable AI for Trees: From Local Explanations to Global Understanding. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAccT 2020), 418-429.
Year 2024, Volume: 5 Issue: 2, 41 - 62
https://doi.org/10.55195/jscai.1576195

Abstract

References

  • Barry, B., Chan, H. A. Barry, B., Chan, H. (2010), Intrusion Detection Systems, In: Stavroulakis, P., Stamp, M. (eds): Handbook of Information and Communication Security pp193-205, SpringerLink. DOI:10.1007/978-3-642-04117-4_10.
  • Ashiku L. and Dagli C.H. (2021). Network Intrusion Detection System using Deep Learning, Procedia Computer Science 2021, 185(1):239-247
  • Gottapu S. R. and Krishna S. P. (2023), A Novel Approach for Detection of DoS / DDoS Attack in Network Environment using Ensemble Machine Learning Model. International Journal on Recent and Innovation Trends in Computing and Communication 11(9):244-253. DOI: 10.17762/ijritcc.v11i9.8340ISBN: 2321-8169
  • Gottapu S. R. and Krishna S. P. (2024), Exploring a novel framework for DoS/DDoS attack detection and simulation in contemporary networks, January 2024i-manager’s Journal on Software Engineering 18(3):43. DOI:10.26634/jse.18.3.20596
  • Inuwa, M. M., & Das, R. (2024). A comparative analysis of various machine learning methods for anomaly detection in cyber-attacks on IoT networks. Internet of Things, 26, 101162. https://doi.org/10.1016/j.iot.2024.101162
  • Becerra-Suarez, F.L., Tuesta-Monteza, V.A., Mejia-Cabrera, H.I., Arcila-Diaz, J. (2024). Performance Evaluation of Deep Learning Models for Classifying Cybersecurity Attacks in IoT Networks. Informatics 2024, 11, 32. https://doi.org/10.3390/informatics11020032
  • Liu, H.; Lang, B. Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey. Appl. Sci. 2019, 9, 4396. https://doi.org/10.3390/app9204396
  • Amutha S., Kavitha R., Srinivasan R. and Kavitha M., "Secure network intrusion detection system using NID-RNN based Deep Learning," 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), Chennai, India, 2022, pp. 1-5, doi: 10.1109/ACCAI53970.2022.9752526.
  • Liao, H., Murah, M. Z., Hasan, M. K., Aman, A. H. M., Fang, J., Hu, X., & Khan, A. U. R. (2024). A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things. IEEE Access. vol.12, pp.4745-4761, 2024.
  • Tossou, S., Qorib, M., & Kacem, T. (2023, October). Anomaly Based Intrusion Detection System: A Deep Learning Approach. In 2023 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE. Doha, Qatar, 2023, pp. 1-6, doi: 10.1109/ISNCC58260.2023.10323740.
  • Rani, S., & Kumar, S. (2023, May). Unleashing the Power of Machine and Deep Learning for Advanced Network Intrusion Detection: An Analysis and Exploration. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) IEEE, Chennai, India, 2023, pp. 1-9, doi: 10.1109/ACCAI58221.2023.10200892.
  • Pandathara A. (2023). A Comprehensive Examination of Literature Exploring the Implementation of Machine Learning to Network Security's Intrusion Detection Systems. International Journal of Advanced Research in Science, Communication and Technology, doi: 10.48175/ijarsct-8605
  • Hussain, A., Sharif, H., Rehman, F., Kirn, H., Sadiq, A., Khan, M. S., Riaz, A., Ali, C. N., & Chandio, A. H. (2023). A systematic review of intrusion detection systems in internet of things using ML and DL. In 2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) (pp. 1-5). IEEE. https://doi.org/10.1109/iCoMET57998.2023.10099142
  • Nakip M., Gül B. C., Gelenbe E. (2023). Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection. In 2023 31st International Symposiumon Modeling, Analysis, and Simulation of Computer andTelecommunication Systems (MASCOTS). IEEE, 2023, pp. 1–8. DOI:10.1109/MASCOTS59514.2023.10387644
  • Zhu, S., Xu, X., Zhao, J., & Xiao, F. (2024). Lkd-stnn: A lightweight malicious traffic detection method for internet of things based on knowledge distillation. IEEE Internet of Things Journal, vol. 11, no. 4, pp. 6438-6453, 15 Feb.15, 2024.
  • Kheddar, H., Himeur, Y., & Awad, A. I. (2023). Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review. Journal of Network and Computer Applications, 220, 103760. https://doi.org/10.1016/j.jnca.2023.103760
  • Sunil, C. K., Reddy, S., Kanber, S. G., Sandeep, V. R., & Patil, N. (2023). Comparative analysis of intrusion detection system using ML and DL techniques. In Hybrid Intelligent Systems (pp. 736-745). Springer, Cham. https://doi.org/10.1007/978-3-031-27409-1_67
  • Mert, Nakip., Baran, Can, Gül., Erol, Gelenbe. (2023). Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection. arXiv.org, doi: 10.48550/arXiv.2306.13029
  • Wasnik P. and Chavhan N., "A Review Paper on Designing Intelligent Intrusion Detection System Using Deep Learning," 2023 11th International Conference on Emerging Trends in Engineering & Technology - Signal and Information Processing (ICETET - SIP), Nagpur, India, 2023, pp. 1-6, doi: 10.1109/ICETET-SIP58143.2023.10151563.
  • Ogundokun R. O., Basil U., Babatunde A. N., Abdulahi A. T., Adenike A. R. and Adebiyi A. A., "Intrusion Detection Systems Based on Machine Learning Approaches: A Systematic Review," 2023 International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG), Omu-Aran, Nigeria, 2023, pp. 01-04, doi: 10.1109/SEB-SDG57117.2023.10124506.
  • Krishna, A., Lal, A., Mathewkutty, A. J., Jacob, D. S., & Hari, M. (2020, July). Intrusion detection and prevention system using deep learning. In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 273-278). IEEE.
  • Fadel, M. M., El-Ghamrawy, S. M., Ali-Eldin, A. M., Hassan, M. K., & El-Desoky, A. I. (2022). HDLIDP: A Hybrid Deep Learning Intrusion Detection and Prevention Framework. Computers, Materials & Continua, 73(2).
  • Alghamdi, Mohammed I., A Hybrid Model for Intrusion Detection in IoT Applications, Wireless Communications and Mobile Computing, 2022, 4553502, 9 pages, 2022. https://doi.org/10.1155/2022/4553502
  • Monani A. Bhusnale O. Borade K. Madali R. (2023). Analysing Cyber Threats: A Comprehensive Literature Review on Data-Driven Approaches, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), Volume 9, Issue 3, pp.188-193, May-June-2023. Available at doi : https://doi.org/10.32628/CSEIT2390351
  • Auwal, Sani, Iliyasu. (2022). A Survey of Network Intrusion Detection Techniques Using Deep Learning. International Journal of Engineering Research in Computer Science and Engineering, doi: 10.36647/ijercse/09.08.art017
  • Hnamte V., Hussain J. (2023). Network Intrusion Detection using Deep Convolution Neural Network. 4th International Conference for Emerging Technology (INCET). doi: 10.1109/INCET57972.2023.10170202
  • Alabdulatif, A., & Rizvi, S.S.H. (2022). Machine learning approach for improvement in kitsune NID. Intelligent Automation & Soft Computing, 32(2), 827-840. https://doi.org/10.32604/iasc.2022.021879
  • Malliga, S., Nandhini, P. S., & Kogilavani, S. V. (2022). A comprehensive review of deep learning techniques for the detection of (distributed) denial of service attacks. Information Technology and Control, doi: 10.5755/j01.itc.51.1.29595
  • Sujatha, V., Prasanna, K. L., Niharika, K., Charishma, V., & Sai, K. B. (2023). Network intrusion detection using deep reinforcement learning. 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), 1146-1150. https://doi.org/10.1109/ICCMC56507.2023.10083673
  • Mohammed, A., Bahashwan, A.A., Manickam, S., Al-Amiedy, T.A., Aladaileh, M.A., & Hasbullah, I.H. (2023). A systematic literature review on machine learning and deep learning approaches for detecting DDoS attacks in software-defined networking. Sensors, 23(9), 4441. https://doi.org/10.3390/s23094441
  • Omarov, B., Asqar, M., Sadybekov, R., Koishiyeva, T., Bazarbayeva, A., & Uxikbayev, Y. (2022). IoT network intrusion detection: A brief review. 2022 International Conference on Smart Information Systems and Technologies (SIST), 1-5. https://doi.org/10.1109/SIST54437.2022.9945763
  • Tahreeem, M., Andleeb, I., Hussain, B. Z., & Hameed, A. (2022, December). Machine learning-based Android intrusion detection systems. Paper presented at the International Conference on Data Intensive Applications & Their Challenges (Computatia X), Jaipur, India. Aligarh Muslim University, University of Windsor, Texas A&M University.
  • Gonaygunta, H. (2023). Machine learning algorithms for detection of cyber threats using logistic regression. International Journal of Smart Sensor and Adhoc Network, 3(4), 36-42. https://doi.org/10.47893/IJSSAN.2023.1229
  • Pandey, G., Kumar, A. K., & Jha, M. (2024). Human activity recognition using CNN-LSTM-GRU model. International Research Journal on Advanced Engineering Hub (IRJAEH), 2(04), 889-894. https://doi.org/10.47392/IRJAEH.2024.012
  • Bhattarai, A., Gyawali, U., Verma, A., & Ranga, V. (2024). Improving intrusion detection in a software-defined network using hybrid CNN and Bi-LSTM. Proceedings of the 2024 IEEE International Conference on Artificial Intelligence and Computational Applications (ICAAIC). https://doi.org/10.1109/icaaic60222.2024.10575090
  • Abdulhakim, A., & Ilyas, M. (2024). Deep learning for smart grid intrusion detection: A hybrid CNN-LSTM-based model. International Journal of Artificial Intelligence & Applications (IJAIA), 15(3), 1-10. https://doi.org/10.5121/ijaia.2024.15301
  • Al-Aql, N. (2024). Hybrid RNN-LSTM networks for enhanced intrusion detection in vehicle CAN systems. Journal of Electrical Systems, 33(1), 1-8. https://doi.org/10.52783/jes.3318
  • Poornachander, V., Kumar, K. S., & Jagadish, S. (2024). DDoS attack intrusion detection system with CNN and LSTM hybridization. Proceedings of the 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS), Coimbatore, India, 1-6. https://doi.org/10.1109/ICSCSS60660.2024.10625330
  • Lv, H., & Ding, Y. (2024). A hybrid intrusion detection system with K-means and CNN+LSTM. EAI Endorsed Transactions on Scalable Information Systems, 11(6). https://doi.org/10.4108/eetsis.5667
  • Abu Khalil, D., & Abuzir, Y. (n.d.). Detecting and Analyzing Network Attacks: A Time-Series Analysis Using the Kitsune Dataset. Journal of Emerging Computer Technologies, 5(1), 9-23. https://doi.org/10.57020/ject.1563146.
  • Gür, Y. E. (2024). Comparative Analysis of Deep Learning Models for Silver Price Prediction: CNN, LSTM, GRU and Hybrid Approach. Akdeniz İİBF Dergisi, 24(1), 1-13. https://doi.org/10.25294/auiibfd.1404173
  • Scikit-learn. (2021). scikit-learn: Machine Learning in Python. Retrieved from https://scikit-learn.org/
  • Hayel, R., Hindi, K. M., Hosny, M. I., & Alharbi, R. (2024). A selective LVQ algorithm for improving instance reduction techniques and its application for text classification. Journal of Intelligent & Fuzzy Systems. https://doi.org/10.3233/JIFS-235290
  • Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning (ICML 2006).
  • Van Rijsbergen, C. J. (1979). Information Retrieval. Butterworth-Heinemann.
  • Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
  • Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS 2017), 4765-4774.
  • Lundberg, S. M., Erion, G., & Lee, S. I. (2020). Explainable AI for Trees: From Local Explanations to Global Understanding. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAccT 2020), 418-429.
There are 48 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Articles
Authors

Dima Raed Abu Khalil 0009-0007-9597-1510

Yousef Abuzir 0000-0002-1220-1411

Early Pub Date December 23, 2024
Publication Date
Submission Date October 30, 2024
Acceptance Date November 25, 2024
Published in Issue Year 2024 Volume: 5 Issue: 2

Cite

APA Khalil, D. R. A., & Abuzir, Y. (2024). Cybersecurity in the Internet of Things: the Detection of the Types of Upcoming Digital Information by Using Classification Techniques. Journal of Soft Computing and Artificial Intelligence, 5(2), 41-62. https://doi.org/10.55195/jscai.1576195
AMA Khalil DRA, Abuzir Y. Cybersecurity in the Internet of Things: the Detection of the Types of Upcoming Digital Information by Using Classification Techniques. JSCAI. December 2024;5(2):41-62. doi:10.55195/jscai.1576195
Chicago Khalil, Dima Raed Abu, and Yousef Abuzir. “Cybersecurity in the Internet of Things: The Detection of the Types of Upcoming Digital Information by Using Classification Techniques”. Journal of Soft Computing and Artificial Intelligence 5, no. 2 (December 2024): 41-62. https://doi.org/10.55195/jscai.1576195.
EndNote Khalil DRA, Abuzir Y (December 1, 2024) Cybersecurity in the Internet of Things: the Detection of the Types of Upcoming Digital Information by Using Classification Techniques. Journal of Soft Computing and Artificial Intelligence 5 2 41–62.
IEEE D. R. A. Khalil and Y. Abuzir, “Cybersecurity in the Internet of Things: the Detection of the Types of Upcoming Digital Information by Using Classification Techniques”, JSCAI, vol. 5, no. 2, pp. 41–62, 2024, doi: 10.55195/jscai.1576195.
ISNAD Khalil, Dima Raed Abu - Abuzir, Yousef. “Cybersecurity in the Internet of Things: The Detection of the Types of Upcoming Digital Information by Using Classification Techniques”. Journal of Soft Computing and Artificial Intelligence 5/2 (December 2024), 41-62. https://doi.org/10.55195/jscai.1576195.
JAMA Khalil DRA, Abuzir Y. Cybersecurity in the Internet of Things: the Detection of the Types of Upcoming Digital Information by Using Classification Techniques. JSCAI. 2024;5:41–62.
MLA Khalil, Dima Raed Abu and Yousef Abuzir. “Cybersecurity in the Internet of Things: The Detection of the Types of Upcoming Digital Information by Using Classification Techniques”. Journal of Soft Computing and Artificial Intelligence, vol. 5, no. 2, 2024, pp. 41-62, doi:10.55195/jscai.1576195.
Vancouver Khalil DRA, Abuzir Y. Cybersecurity in the Internet of Things: the Detection of the Types of Upcoming Digital Information by Using Classification Techniques. JSCAI. 2024;5(2):41-62.