How to cite this paper
Santoso, J., Wibowo, M & Raharjo, B. (2024). Innovative IoT security protocol: High-accuracy device identification and resilience against credential compromise (HADIRACC).International Journal of Data and Network Science, 8(4), 2639-2650.
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Chataut, R., Phoummalayvane, A., & Akl, R. (2023). Unleashing the Power of IoT: A Comprehensive Review of IoT Ap-plications and Future Prospects in Healthcare, Agriculture, Smart Homes, Smart Cities, and Industry 4.0. Sensors, 23(16), 7194. https://doi.org/10.3390/s23167194
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Kumar, A., Saha, R., Conti, M., Kumar, G., Buchanan, W. J., & Kim, T. H. (2022). A comprehensive survey of authentica-tion methods in Internet-of-Things and its conjunctions. Journal of Network and Computer Applications, 204, 103414. https://doi.org/10.1016/j.jnca.2022.103414
Liu, P., Ji, S., Fu, L., Lu, K., Zhang, X., Qin, J., Wang, W., & Chen, W. (2023). How IoT Re-using Threatens Your Sensi-tive data: Exploring the User-Data Disposal in Used IoT Devices. 2023 IEEE Symposium on Security and Privacy (SP), 3365–3381. https://doi.org/10.1109/SP46215.2023.10179294
Omolara, A. E., Alabdulatif, A., Abiodun, O. I., Alawida, M., Alabdulatif, A., Alshoura, W. H., & Arshad, H. (2022). The internet of things security: A survey encompassing unexplored areas and new insights. Computers & Security, 112, 102494. https://doi.org/10.1016/j.cose.2021.102494
Ravikumar, K. C., Chiranjeevi, P., Manikanda Devarajan, N., Kaur, C., & Taloba, A. I. (2022). Challenges in internet of things towards the security using deep learning techniques. Measurement: Sensors, 24, 100473. https://doi.org/10.1016/j.measen.2022.100473
Rawat, A. (2022). Recent Trends in IoT : A review. Journal of Management and Service Science (JMSS), 2(2), 1–12. https://doi.org/10.54060/jmss.v2i2.21
Saba, T., Haseeb, K., Shah, A. A., Rehman, A., Tariq, U., & Mehmood, Z. (2021). A Machine-Learning-Based Approach for Autonomous IoT Security. IT Professional, 23(3), 69–75. https://doi.org/10.1109/MITP.2020.3031358
Salman, O., Elhajj, I. H., Chehab, A., & Kayssi, A. (2022). A machine learning based framework for IoT device identifica-tion and abnormal traffic detection. Transactions on Emerging Telecommunications Technologies, 33(3). https://doi.org/10.1002/ett.3743
Scikit-learn: machine learning in Python — scikit-learn 1.3.2 documentation. (n.d.). Retrieved December 29, 2023, from available: https://scikit-learn.org/stable/
Sobot, S., Ninkovic, V., Vukobratovic, D., Pavlovic, M., & Radovanovic, M. (2022). Machine Learning Methods for De-vice Identification Using Wireless Fingerprinting. 2022 International Balkan Conference on Communications and Networking (BalkanCom), 183–188. https://doi.org/10.1109/BalkanCom55633.2022.9900723
Tomer, V., & Sharma, S. (2022). Detecting IoT Attacks Using an Ensemble Machine Learning Model. Future Internet, 14(4), 102. https://doi.org/10.3390/fi14040102
Troscia, M., Sgambelluri, A., Paolucci, F., Castoldi, P., Pagano, P., & Cugini, F. (2022). Scalable OneM2M IoT Open-Source Platform Evaluated in an SDN Optical Network Controller Scenario. Sensors, 22(2), 431. https://doi.org/10.3390/s22020431
Yu, J., Lian, H., Zhao, Z., Tang, Y., & Wang, X. (2021). Provably secure verifier-based password authenticated key ex-change based on lattices (pp. 121–156). https://doi.org/10.1016/bs.adcom.2020.09.003
Zhang, Y., Han, D., Li, A., Li, J., Li, T., & Zhang, Y. (2023). SmartMagnet: Proximity-Based Access Control for IoT De-vices With Smartphones and Magnets. IEEE Transactions on Mobile Computing, 22(7), 4266–4278. https://doi.org/10.1109/TMC.2022.3149746
Zhou, L., & Wang, H. (2022). A Combined Feature Screening Approach of Random Forest and Filterbased Methods for Ultra-high Dimensional Data. Current Bioinformatics, 17(4), 344–357. https://doi.org/10.2174/1574893617666220221120618
Bao, J., Hamdaoui, B., & Wong, W.-K. (2020). IoT Device Type Identification Using Hybrid Deep Learning Approach for Increased IoT Security. 2020 International Wireless Communications and Mobile Computing (IWCMC), 565–570. https://doi.org/10.1109/IWCMC48107.2020.9148110
Barua, A., Al Alamin, M. A., Hossain, M. S., & Hossain, E. (2022). Security and Privacy Threats for Bluetooth Low Ener-gy in IoT and Wearable Devices: A Comprehensive Survey. IEEE Open Journal of the Communications Society, 3, 251–281. https://doi.org/10.1109/OJCOMS.2022.3149732
Chataut, R., Phoummalayvane, A., & Akl, R. (2023). Unleashing the Power of IoT: A Comprehensive Review of IoT Ap-plications and Future Prospects in Healthcare, Agriculture, Smart Homes, Smart Cities, and Industry 4.0. Sensors, 23(16), 7194. https://doi.org/10.3390/s23167194
EZVIZ - Creating Easy Smart Homes. (n.d.). Retrieved December 31, 2023, from available: https://www.ezviz.com/id
Ghose, N., Gupta, K., Lazos, L., Li, M., Xu, Z., & Li, J. (2024). ZITA: Zero-Interaction Two-Factor Authentication using Contact Traces and In-band Proximity Verification. IEEE Transactions on Mobile Computing, 1–16. https://doi.org/10.1109/TMC.2023.3321514
Hansdah, R. C., Jamwal, J., & Gudivada, R. B. (2022). Dragonshield : An Authentication Enhancement for Mitigating Side-Channel Attacks and High Computation Overhead in WPA3-SAE Handshake Protocol. Proceedings of the 23rd International Conference on Distributed Computing and Networking, 188–197. https://doi.org/10.1145/3491003.3491021
He, Y., Zeng, K., Mark, B. L., & Khasawneh, K. N. (2022). Secure and Energy-Efficient Proximity-Based Pairing for IoT Devices. 2022 IEEE Globecom Workshops (GC Wkshps), 1359–1364. https://doi.org/10.1109/GCWkshps56602.2022.10008568
Jeon, S., & Kim, H. K. (2021). AutoVAS: An automated vulnerability analysis system with a deep learning approach. Computers & Security, 106, 102308. https://doi.org/10.1016/j.cose.2021.102308
Kumar, A., Abhishek, K., Ghalib, M. R., Shankar, A., & Cheng, X. (2022). Intrusion detection and prevention system for an IoT environment. Digital Communications and Networks, 8(4), 540–551. https://doi.org/10.1016/j.dcan.2022.05.027
Kumar, A., Saha, R., Conti, M., Kumar, G., Buchanan, W. J., & Kim, T. H. (2022). A comprehensive survey of authentica-tion methods in Internet-of-Things and its conjunctions. Journal of Network and Computer Applications, 204, 103414. https://doi.org/10.1016/j.jnca.2022.103414
Liu, P., Ji, S., Fu, L., Lu, K., Zhang, X., Qin, J., Wang, W., & Chen, W. (2023). How IoT Re-using Threatens Your Sensi-tive data: Exploring the User-Data Disposal in Used IoT Devices. 2023 IEEE Symposium on Security and Privacy (SP), 3365–3381. https://doi.org/10.1109/SP46215.2023.10179294
Omolara, A. E., Alabdulatif, A., Abiodun, O. I., Alawida, M., Alabdulatif, A., Alshoura, W. H., & Arshad, H. (2022). The internet of things security: A survey encompassing unexplored areas and new insights. Computers & Security, 112, 102494. https://doi.org/10.1016/j.cose.2021.102494
Ravikumar, K. C., Chiranjeevi, P., Manikanda Devarajan, N., Kaur, C., & Taloba, A. I. (2022). Challenges in internet of things towards the security using deep learning techniques. Measurement: Sensors, 24, 100473. https://doi.org/10.1016/j.measen.2022.100473
Rawat, A. (2022). Recent Trends in IoT : A review. Journal of Management and Service Science (JMSS), 2(2), 1–12. https://doi.org/10.54060/jmss.v2i2.21
Saba, T., Haseeb, K., Shah, A. A., Rehman, A., Tariq, U., & Mehmood, Z. (2021). A Machine-Learning-Based Approach for Autonomous IoT Security. IT Professional, 23(3), 69–75. https://doi.org/10.1109/MITP.2020.3031358
Salman, O., Elhajj, I. H., Chehab, A., & Kayssi, A. (2022). A machine learning based framework for IoT device identifica-tion and abnormal traffic detection. Transactions on Emerging Telecommunications Technologies, 33(3). https://doi.org/10.1002/ett.3743
Scikit-learn: machine learning in Python — scikit-learn 1.3.2 documentation. (n.d.). Retrieved December 29, 2023, from available: https://scikit-learn.org/stable/
Sobot, S., Ninkovic, V., Vukobratovic, D., Pavlovic, M., & Radovanovic, M. (2022). Machine Learning Methods for De-vice Identification Using Wireless Fingerprinting. 2022 International Balkan Conference on Communications and Networking (BalkanCom), 183–188. https://doi.org/10.1109/BalkanCom55633.2022.9900723
Tomer, V., & Sharma, S. (2022). Detecting IoT Attacks Using an Ensemble Machine Learning Model. Future Internet, 14(4), 102. https://doi.org/10.3390/fi14040102
Troscia, M., Sgambelluri, A., Paolucci, F., Castoldi, P., Pagano, P., & Cugini, F. (2022). Scalable OneM2M IoT Open-Source Platform Evaluated in an SDN Optical Network Controller Scenario. Sensors, 22(2), 431. https://doi.org/10.3390/s22020431
Yu, J., Lian, H., Zhao, Z., Tang, Y., & Wang, X. (2021). Provably secure verifier-based password authenticated key ex-change based on lattices (pp. 121–156). https://doi.org/10.1016/bs.adcom.2020.09.003
Zhang, Y., Han, D., Li, A., Li, J., Li, T., & Zhang, Y. (2023). SmartMagnet: Proximity-Based Access Control for IoT De-vices With Smartphones and Magnets. IEEE Transactions on Mobile Computing, 22(7), 4266–4278. https://doi.org/10.1109/TMC.2022.3149746
Zhou, L., & Wang, H. (2022). A Combined Feature Screening Approach of Random Forest and Filterbased Methods for Ultra-high Dimensional Data. Current Bioinformatics, 17(4), 344–357. https://doi.org/10.2174/1574893617666220221120618