Hybrid Machine Learning Model for Real-Time Anomaly Detection in IOT Networks
Abstract
As the number of Internet of Things devices connected continues to rise rapidly, a negative consequence will be the emergence of new types of attack vectors. The growing number of connected devices generates an ever-increasing volume of traffic across the Internet, exposing these devices to the threat of malicious attacks. Traditional methods used to identify anomalous behaviours (for example, detecting unusual events) are unable to work effectively on the volume of data generated by IoT devices due to their sensitivity to noise, their tendency to have class imbalances, and their inability to model very complex data patterns. In this study, we propose the use of a hybrid machine-learning model based on Support Vector Machines and Isolation Forest to enable us to identify anomalous behaviour in real time with a higher degree of accuracy compared to using either method independently. We implemented the hybrid approach using Python and tested it on 10,000 flow records created from actual network activity (80% for training, 20% for testing). Hybrid Algorithm Results were superior to results achieved by using each algorithm alone, with hybrid achieving Accuracy of 98.3%, Precision of 98.0%, Sensitivity of 98.5%, and F1 Score of 98.2%, and AUC for Hybrid was equal to 0.99, which is greater than SVM alone (94.2%, AUC = 0.96) or Isolation Forest Algorithm alone (91.5%, 93), Therefore, using complementary algorithms combined provide significantly improved detection rate with greatly reduced number of false alerts, thus providing higher level of confidence in deploying in healthcare, industrial automation and smart cities.
Keywords
Anomaly DetectionHybrid Machine LearningIoT SecurityIsolation ForestSupport Vector Machine
References
Adhikari, D., Jiang, W., Zhan, J., Rawat, D. B., & Bhattarai, A. (2024). Recent Advances in Anomaly Detection in Internet of Things: Status, Challenges and Perspectives. Computer Science Review, 54, 1–9. https://doi.org/10.1016/j.cosrev.2024.100665 Ahmed, U., Nazir, M., Sarwar, A., Ali, T., Aggoune, E. H. M., Shahzad, T., & Khan, M. A. (2025). Signature-Based Intrusion Detection using Machine Learning and Deep Learning Approaches Empowered with Fuzzy Clustering. In Scientific Reports. https://doi.org/10.1038/s41598-025-85866-7 Alghanmi, N., Alotaibi, R., & Buhari, S. M. (2024). Machine Learning Approaches for Anomaly Detection in IoT: An Overview and Future Research Directions. Wireless Personal Communications, 1–16. https://doi.org/10.1007/s11277-021-08994-z Anderson, J., Johnson, E., & Brown, M. (2024). IoT, Anomaly Detection, Machine Learning, K- Nearest Neighbors, Random Forest, Real-Time Detection. International Journal of Information Engineering and Science, 1(1), 3048–1902. https://doi.org/10.62951/ijies.v1i2.50Availableonlineat:https://international.arteii.or.id/in dex.php/IJIES Arslan, F., Javaid, A., Danish Zaheer Awan, M., & Ebad-ur-Rehman. (2024). Anomaly Detection in Time Series: Current Focus and Future Challenges. In K. P. Venkata (Ed.), Anomaly Detection: Recent Advances, AI and ML Perspectives and Applications (pp. 1–22). IntechOpen. https://doi.org/10.5772/intechopen.111886 Ayad, A. G., El-Gayar, M. M., Hikal, N. A., & Sakr, N. A. (2025). Efficient Real-Time Anomaly Detection in IoT Networks Using One-Class Autoencoder and Deep Neural Network. Electronics (Switzerland), 14(104), 1–25. https://doi.org/10.3390/electronics14010104 Azrour, M., Mabrouki, J., Guezzaz, A., & Kanwal, A. (2021). Internet of Things Security: Challenges and Key Issues. Security and Communication Networks, 1–11. https://doi.org/10.1155/2021/5533843 Baranwal, T., Das, A., Varada, S., Das, S., & Haider, M. R. (2025). Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach. http://arxiv.org/abs/2505.24044 Bibers, I., & Abdallah, M. (2025). An Ensemble Learning Framework for Enhanced Anomaly and Failure Detection in IoT Systems. Cyber Security and Applications, 3, 1–23. https://doi.org/10.1016/j.csa.2025.100105 Blanton, S. (2025). IoT Security Risks: Stats and Trends to Know in 2025. In Best Practices. https://jumpcloud.com/blog/iot-security-risks-stats-and-trends-to-know-in- 2025#:~:text=More than 50%25 of IoT,(IoT Security Foundation) Boujrad, M., Kasmi, M. A., Ouerdi, N., & El Mazoui Nadori, Y. L. (2025). Anomaly Based Detection in Time Series Data on IoT Systems using Statistical Models. Indonesian Journal of Electrical Engineering and Computer Science, 37(3), 1845–1854. https://doi.org/10.11591/ijeecs.v37.i3.pp1845-1855 Choudhary, A. (2024). Internet of Things: a Comprehensive Overview, Architectures, Applications, Simulation Tools, Challenges and Future Directions. Discover Internet of Things, 4(31), 1–41. https://doi.org/10.1007/s43926-024-00084-3 Dagli, M. K., Padheriya, D. H., Devani, M., & Patel, J. (2025). A Survey Paper based: Network Anomaly Detection for IoT Systems in Smart City using Machine Learning Approach. African Journal of Biomedical Research, 28(2), 1641–1651. https://doi.org/10.53555/ajbr.v28i2s.7290 IJCSMT Deshpande, Y. D., & Rahman, S. R. (2023). Edge-Based Real-Time Sensor Data Processing for Anomaly Detection in Industrial IoT Applications. Research Journal of Computer Systems and Engineering, 4(2), 16–30. https://doi.org/10.52710/rjcse.71 Domb, M., Joshi, S., & Khn, A. (2024). Anomaly Detection in IoT: Recent Advances, AI and ML Perspectives and Applications. In K. P. Venkata (Ed.), Anomaly Detection - Recent Advances, AI and ML Perspectives and Applications (pp. 1–24). IntechOpen. https://doi.org/10.5772/intechopen.111944 Falkner, S. (2025). Anomaly Detection in IoT Using Machine Learning. Advances in Artificial Intelligence & Data Science and Machine Learning, 1(1), 1–11. Gilad, D. M. (2024). Top 10 IoT Security Challenges and Solutions. Cyber Security Intelligence. Ginni, G. R., & Chakravarthy, S. L. (2025). A Hybrid Framework for Robust Anomaly Detection: Integrating Unsupervised and Supervised Learning with Advanced Feature Engineering. International Journal of Computational and Experimental Science and Engineering, 11(2), 1993–2017. https://doi.org/10.22399/ijcesen.1383 Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58. Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19–31. Al-Qurishi, M., Ameen, A., & Khan, F. (2021). IoT intrusion detection: Hybrid ML approaches. IEEE Access, 9, 145678–145692. Kim, Y., Kang, B., & Lee, J. (2018). IoT security with hybrid ML-based anomaly detection. Sensors, 18(11), 3801. Tavallaee, M., Stakhanova, N., & Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 data set. Proceedings of the Second IEEE Symposium on Computational Intelligence for Security and Defense Applications. Nguyen, H., Choi, D., & Kim, S. (2020). Hybrid machine learning approaches for real-time IoT intrusion detection. Future Generation Computer Systems, 102, 1080–1092. Green, O. O., Yusuf, S. M., Abdulrazaq, M. B., Yahaya, B., Haruna, Z., Ore-Ofe, A., Adegoke, A. S., & Salami, S. O. (2025). Anomaly Detection in a Portal System Network: A Review of Techniques, Challenges and Future Directions. https://doi.org/10.19044/esipreprint.7.2025.p638 Gupta, P., & Tripathy, P. (2024). Unsupervised Learning for Real-Time Data Anomaly Detection: A Comprehensive Approach. International Journal of Computer Science and Engineering, 11(10), 1–11. https://doi.org/10.14445/23488387/ijcse-v11i10p101 Haider, Z. A., Zeb, A., Rahman, T., Singh, S. K., Akram, R., Arishi, A., & Ullah, I. (2025). A Survey on Anomaly Detection in IoT: Techniques, Challenges and Opportunities with the Integration of 6G. Computer Networks, 270, 1–30. https://doi.org/10.1016/j.comnet.2025.111484 Hammad, A. A., Falih, M. A., Ali Abd, S., & Ahmed, A. R. (2025). Detecting Cyber Threats in IoT Networks: A Machine Learning Approach. International Journal of Computing and Digital Systems, 17(1), 1–25. https://doi.org/10.12785/ijcds/1571020041 Haque, A., & Soliman, H. (2025). A Transformer-Based Autoencoder with Isolation Forest and XGBoost for Malfunction and Intrusion Detection in Wireless Sensor Networks for Forest Fire Prediction. Future Internet, 17(164), 1–12. https://doi.org/Future Internet 2025, 17, 164 https://doi.org/10.3390/fi17040164 Harahsheh, K. (2025). Enhancing IoT Security Using Lightweight Machine Learning Algorithms : IJCSMT A Comprehensive Approach Using Ensemble Learning, Feature Selection and Federated Transfer Learning [Old Dominion University]. https://doi.org/10.25777/cnsc-h573 Harahsheh, K., & Chen, C. H. (2023). A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers. Informatica (Slovenia), 47, 1–54. https://doi.org/10.31449/inf.v47i6.4635 He, S., Guo, M., Yang, B., Alfarraj, O., Tolba, A., Sharma, P. K., & Yan, X. (2023). Fine-Grained Multivariate Time Series Anomaly Detection in IoT. Computers, Materials and Continua, 75(3), 5027–5047. https://doi.org/10.32604/cmc.2023.038551 Herrman, H., Patel, V., Kieling, C., Berk, M., Buchweitz, C., Cuijpers, P., Furukawa, A. T., Kessler, C. R., Maj, M., McGorry, P., Reynolds, F. C., Weissman, M. M., Chibanda, D., Dowrick, C., Howard, M. L., Hoven, W. C., Knapp, M., Kohrt, A. B., Mayberg, S. H., ... Wolpert, M. (2021). Time For A United Action On Depression. The Lancet, 1–125. Hind, K. (2025). Anomaly Detection in IoT Networks using Machine Learning Techniques. Dijlah Journal of Engineering Science , 2(3), 127–136. https://doi.org/10.1109/IC3SIS54991.2022.9885575 Jaafouri, L., Et-Tolba, M., Hanin, C., & Lahsen-Cherif, I. (2025). IoT Network Security : Ensemble-Based Approaches for Anomaly Detection. 2025 International Conference on Circuit, Systems and Communication , 1–6. https://doi.org/10.1109/ICCSC66714.2025.11135113 Janraol, A. A., Saatarkar, P. A., Dhumal, T. A., Pawar, S. P., & Bhatlavande, A. S. (2025). Hybrid Machine Learning Models for Intrusion Detection: Combining Supervised and Unsupervised Techniques. Power Systems Technology, 49(3), 1–16. Jeffrey, N., Tan, Q., & Villar, J. R. (2024a). A Hybrid Methodology for Anomaly Detection in Cyber–Physical Systems. Neurocomputing, 568, 1–7. https://doi.org/10.1016/j.neucom.2023.127068 Jeffrey, N., Tan, Q., & Villar, J. R. (2024b). Using Ensemble Learning for Anomaly Detection in Cyber–Physical Systems. Electronics (Switzerland), 13(1391), 1–17. https://doi.org/10.3390/electronics13071391 Kamal, H., & Mashaly, M. (2025). Enhanced Hybrid Deep Learning Models-Based Anomaly Detection Method for Two-Stage Binary and Multi-Class Classification of Attacks in Intrusion Detection Systems. Algorithms, 18(69), 1–59. https://doi.org/10.3390/a18020069 Karne, N. (2023). Hybrid Machine Learning Models for Real-Time Anomaly Detection in Complex Deployment Environments. International Journal of Scientific Research in Science and Technology, 10(2), 1040–1052. https://doi.org/10.32628/ijsrst523102184 Kaspersky. (2025). Internet of Things Security Challenges and Best Practices. Preemptive Safety. https://www.kaspersky.com/resource-center/preemptive-safety/best-practices-for-iot- security Khan, F., Kumar, B. S. S., & Sangani, S. (2025). A Feature-level Ensemble Machine Learning Approach for Attack Detection in IoT Networks. Discover Internet of Things, 5(81), 1–24. https://doi.org/10.1007/s43926-025-00185-7 Kikissagbe, B. R., & Adda, M. (2024). Machine Learning-Based Intrusion Detection Methods in IoT Systems: A Comprehensive Review. Electronics (Switzerland), 13(3601), 1–23. https://doi.org/10.3390/electronics13183601 Kohli, M., & Chhabra, I. (2025). A Comprehensive Survey on Techniques, Challenges, Evaluation Metrics and Applications of Deep Learning Models for Anomaly Detection. Discover Applied Sciences, 7(784), 1–29. https://doi.org/10.1007/s42452-025-07312-7 IJCSMT Kontagora, M. M., & Idoko, B. (2024). Machine Learning Algorithms for Anomaly Detection in IoT Networks – A Review. African Multidisciplinary Journal of Sciences and Artificial Intelligence, 1(2), 802–823. https://doi.org/https://doi.org/10.58578/AMJSAI.v1i2.4014 Koroma, M., Mansaray, A., Kamara, Y. L., Jalloh, C. G., & Bah, I. S. O. (2025). Enhancing Cybersecurity in IoT & IIoT: A Machine Learning Approach for Anomaly Detection. Journal of Software Engineering and Applications, 18(6), 175–193. https://doi.org/10.4236/jsea.2025.186012 Krzysztoń, E., Rojek, I., & Mikołajewski, D. (2024). A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study. Applied Sciences (Switzerland), 14(11545), 1–22. https://doi.org/10.3390/app142411545 Kusumastuti, A., Rangelov, D., Lämmel, P., Boerger, M., Aleksandrov, A., & Tcholtchev, N. (2025). Anomaly Detection in IoT Networks: A Performance Comparison of Transformer, 1D-CNN, and GrowNet Models on the Bot-IoT Dataset. Proceedings of the 14th International Conference on Data Science, Technology and Applications (DATA 2025), 633–643. https://doi.org/10.5220/0013637600003967 Laghari, A. A., Li, H., Khan, A. A., Shoulin, Y., Karim, S., & Khani, M. A. K. (2024). Internet of Things Applications Security Trends and Challenges. Discover Internet of Things, 4(36), 1–22. https://doi.org/10.1007/s43926-024-00090-5 Lai, T., Farid, F., Bello, A., & Sabrina, F. (2024). Ensemble Learning Based Anomaly Detection for IoT Cybersecurity Via Bayesian Hyperparameters Sensitivity Analysis. Cybersecurity, 7(44), 1–18. https://doi.org/10.1186/s42400-024-00238-4 Lakhani, R. (2023). Cybersecurity Threats in Internet of Things Networks: Vulnerabilities and Defense Mechanisms. International Journal of Engineering and Computer Science, 12(11), 25965–25980. https://doi.org/10.18535/ijecs/v12i11.4779 Li, E., Gungor, O., Shang, Z., & Rosing, T. (2025). CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection. http://arxiv.org/abs/2508.19450 Liu, L., Yu, Y., Chen, G., Hu, J., & Wu, Y. (2025). Online Ensemble Learning-Based Anomaly Detection for IoT Systems. Applied Soft Computing, 173(112931), 1–20. https://doi.org/10.1016/j.asoc.2025.112931 Lorenzo, D., Dini, P., & Paolini, D. (2025). Overview on Intrusion Detection Systems for Computers Networking Security. Computers , 14(87), 1–44. https://doi.org/https://doi.org/10.3390/computers14030087 Lu, H. (2025). Evaluating the Performance of SVM, Isolation Forest, and DBSCAN for Anomaly Detection. ITM Web of Conferences, 70, 1–6. https://doi.org/10.1051/itmconf/20257004012 Mohammad, A. H., & Husien, M. I. (2024). Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments. Wasit Journal of Computer and Mathematics Science, 3(4), 62–77. https://doi.org/10.31185/wjcms.323 Mohammed, M. Q., Al-Safi, M. G. S., & Faris, A. M. (2024). Statistical Anomaly Detection for Enhanced Cybersecurity Using AI-Based Wireless Networks. Ingenierie Des Systemes d’Information, 29(5), 1743–1754. https://doi.org/10.18280/isi.290508 Muntean, M. V. (2024). Real-Time Detection of IoT Anomalies and Intrusion Data in Smart Cities Using Multi-Agent System. Sensors , 24(7886), 1–17. https://doi.org/10.3390/s24247886 Osasu, V. E., Seyi, B. O., & Nnamdi, C. O. (2025). A Hybrid CNN-LSTM and ADABOOST Model for Classifying Intrusion in IoT Networks. FUDMA Journal of Science, 9(5), 204– IJCSMT 212. https://doi.org/: https://doi.org/10.33003/fjs-2025-0905-3495 8 FJS Palakurti, N. R. (2024). Challenges and Future Directions in Anomaly Detection. In P. Whig, S. Sharma, S. Sharma, A. Jain, & N. Yathiraju (Eds.), Practical Applications of Data Processing, Algorithms, and Modeling (pp. 269–284). IGI Global. https://doi.org/10.4018/979-8-3693-2909-2.ch020 Park, H., Shin, D., Park, C., Jang, J., & Shin, D. (2025). Unsupervised Machine Learning Methods for Anomaly Detection in Network Packets. Electronics (Switzerland), 14(2779), 1–14. https://doi.org/10.3390/electronics14142779 Phalaagae, P., Zungeru, A. M., Yahya, A., Sigweni, B., & Rajalakshmi, S. (2025). A Hybrid CNN- LSTM Model with Attention Mechanism for Improved Intrusion Detection in Wireless IoT Sensor Networks. IEEE Access, 13, 1–21. https://doi.org/10.1109/ACCESS.2025.3555861 Ponnusamy, V., Tiwari, S., Sinha, A., & Kisić, E. (2024). Machine Learning-Driven Anomaly Detection for Enhanced IoT Networks Security. International Conference on Business Information Security, 3971, 135–144. Prakash, V., Odedina, O., Kumar, A., Garg, L., & Bawa, S. (2024). A Secure Framework for the Internet of Things Anomalies using Machine Learning. Discover Internet of Things, 4(33), 1–32. https://doi.org/10.1007/s43926-024-00088-z Punia, A., Tiwari, M., & Verma, S. S. (2025). A Machine Learning-based Efficient Anomaly Detection System for Enhanced Security in Compromised and Maligned IoT Networks. Results in Engineering, 26, 1–16. https://doi.org/10.1016/j.rineng.2025.105562 Raeiszadeh, M., Ebrahimzadeh, A., Glitho, R. H., Eker, J., & Mini, R. A. F. (2024). Real-Time Adaptive Anomaly Detection in Industrial IoT Environments. IEEE Transactions on Network and Service Management, 21(6), 6839–6856. https://doi.org/10.1109/TNSM.2024.3447532 Rafique, S. H., Abdallah, A., Musa, N. S., & Murugan, T. (2024). Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection: Current Research Trends. Sensors MDPI, 24(1968), 1–32. Rashid, M., Haque, M. M., & Wang, W. (2024). IoT Complexity: Security, Vulnerabilities and Risks. European Journal of Electrical Engineering and Computer Science, 8(1), 1–9. https://doi.org/10.24018/ejece.2024.8.1.597 Ravindran, K. V., Ojha, S. S., & Kamboj, A. (2025). A Comparative Analysis Signature-Based and Anomaly-Based Intrusion Detection System. International Journal of Latest Technology in Engineering, Management & Applied Science, XIV(V), 209–214. https://doi.org/DOI: 10.51583/IJLTEMAS Reis, M. J. C. S. (2025). AI-Driven Anomaly Detection for Securing IoT Devices in 5G-Enabled Smart Cities. Electronics (Switzerland), 14(2492), 1–35. https://doi.org/10.3390/electronics14122492 Rhachi, H., Balboul, Y., & Bouayad, A. (2025). Enhanced Anomaly Detection in IoT Networks Using Deep Autoencoders with Feature Selection Techniques. Sensors , 25(3150), 1–21. https://doi.org/10.3390/s25103150 Rosero-Montalvo, P. D., Istvan, Z., Tozun, P., & Hernandez, W. (2023). Hybrid Anomaly Detection Model on Trusted IoT Devices. IEEE Internet of Things Journal, 10(12), 10959– 10969. https://doi.org/10.1109/JIOT.2023.3243037 Saini, H., & Prasad, S. K. (2024). Machine Learning Approaches to Securing IoT Networks: A Comprehensive Review of Recent Advances. 2nd International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024, 1–9. IJCSMT https://doi.org/10.1109/AKGEC62572.2024.10868830 Sarma, W., Srivastava, A., & Sresth, V. (2023). Machine Learning-Based Anomaly Detection in IoT Security: A Comparative Analysis of Supervised and Unsupervised Models. World Journal of Advanced Engineering Technology and Services, 9(2), 377–390. https://doi.org/: https://doi.org/10.30574/wjaets.2023.9.2.0207 Abstract Sebestyen, H., Popescu, D. E., & Zmaranda, R. D. (2025). A Literature Review on Security in the Internet of Things: Identifying and Analysing Critical Categories. Computers, 14(61), 1– 45. https://doi.org/10.3390/computers14020061 Sharma, S., Verma, P., Bharot, N., Kumar, A., Rathore, N. S., & Sharma, S. (2025). A Combined Supervised and Unsupervised Deep Learning Approach for Intrusion Detection in IoT Traffic in an Edge Computing Environment. SN Computer Science, 6(6), 1–20. https://doi.org/10.1007/s42979-025-04103-0 Singh, A., Singh, S., Alam, M. N., & Singh, G. (2025). Deep Learning for Anomaly Detection in IoT Systems: Techniques, Applications, and Future Directions. International Research Journal of Multidisciplinary, 6(4), 1–10. https://doi.org/10.47857/irjms.2025.v06i02.03768 Sinha, P., Sahu, D., Prakash, S., Yang, T., Rathore, R. S., & Pandey, V. K. (2025). A High Performance Hybrid LSTM CNN Secure Architecture for IoT Environments using Deep Learning. Scientific Reports, 15, 1–26. https://doi.org/10.1038/s41598-025-94500-5 Tageldin, L. (2025). Internet of Things Security: Threats, Recent Trends, and Mitigation Approaches. Advances in Internet of Things, 15(01), 1–15. https://doi.org/10.4236/ait.2025.151001 Tejaswini, R., Abinaya, P., Anuprabha, S. S., Chidambaram, S. K., Arya, S., Choukiker, Y. K., & Bhowmick, A. (2025). Anomaly Detection in IoT Networks: A Deep Learning Approach Using Autoencoders. IEEE Access, 13, 132739–132749. https://doi.org/10.1109/ACCESS.2025.3587727 Valiant, L. G. (1984). A Theory of the Learnable. Communication of the ACM, 27(11), 1134– 1142. https://doi.org/10.1145/1968.1972 Waheed, S., Hanif, S., Hafeez, R., Sharif, M. I., Siddique, K., & Akhtar, Z. (2024). A Comprehensive Survey on Applications, Challenges, Threats and Solutions in IoT Environment and Architecture. Journal of Computer Science, 20(3), 310–332. https://doi.org/10.3844/jcssp.2024.310.332 Wang, M., Sun, Y., Sun, H., & Zhang, B. (2023). Security Issues on Industrial Internet of Things: Overview and Challenges. Computers, 12(256), 1–27. https://doi.org/10.3390/computers12120256 Weyori, B. A., Beklisi, B., Ayawli, K., & Tweneboah-koduah, S. (2024). Network Anomaly Detection in the Internet of Things ( IoT ). 45(3), 3753–3769. Wu, J. Y., Wang, Y., Ching, C. T. S., Wang, H. M. D., & Liao, L. De. (2023). IoT-Based Wearable Health Monitoring Device and its Validation for Potential Critical and Emergency Applications. Frontiers in Public Health, 11, 1–13. https://doi.org/10.3389/fpubh.2023.1188304 Zanjani, S. M., Shahinzadeh, H., Kargar, S. M., Moazzami, M., Ebrahimi, F., & Hemmati, M. (2023). Internet of Things Security: A Review on Challenges, Solutions and Research Directions. 7th International Conference on Internet of Things and Applications, IoT 2023, 1–9. https://doi.org/10.1109/IoT60973.2023.10365381
More Articles from INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY
Virtual Environments of Make Virtual Reality Applications Useful
Author: Ghaliya AlFarsi
Convolutional Neural Network-Based Detection of Facial Image Forgeries Using Binary Classification
Author: Ahmed Samaila1, Etemi. J. Garba2, and Oluborode Kayode O., Corresponding author
Transforming Mathematics Teacher Education Through Artificial Intelligence and Digital Innovation: Extent of Utilization, Challenges, and Strategies in Colleges of Education, Gombe State
Author: Yunusa, Samson PhD, Etuk Emmanuel Dan, Micah Michael, and Ismail Uzairu Usman
Certificate Authentication and Revocation System for IoT Devices using CoAP and CBOR Encoding in LoRaWAN
Author: Duke, Tamunotonye Juliana, Sako, D.J.S., Bennett, E.O.
Multiclass Chest X-Ray Classification Using Transfer Learning with Resnet50: Performance Evaluation Across Varying Train-Test Configurations
Author: Joshua Kolapo Awe,, Samson Afolabi Arekete, Olufade Falade Williams Onifade, Olufunmilayo Olopade,, Benjamin Segun Aribisala