Addressing IoT Security Challenges through Advanced Machine Learning and Encryption
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Abstract
The rapid growth of Internet of Things (IoT) devices, including smartwatches, home assistants, and connected appliances, has brought significant convenience to daily life, but it has also introduced serious security challenges. These devices often transmit sensitive data, making them vulnerable to theft, misuse, and unauthorized access. Current security measures are insufficient to address the complex and evolving nature of IoT systems, leaving many of them exposed to potential breaches and cyberattacks. This review explores recent developments in IoT security, focusing on how advanced technologies, such as machine learning, can be utilized to enhance the protection of IoT systems. The main objective of this paper is to examine potential solutions to the security problems that arise in IoT environments. It includes a thorough analysis of recent research and technological innovations in the field, with a particular emphasis on how different security methods are applied across IoT systems. By identifying the most common security vulnerabilities and outlining their impact on IoT networks, the review suggests improved methods to safeguard IoT data and ensure privacy. The findings aim to support researchers, developers, and businesses in designing more secure IoT solutions, and contribute to the establishment of stronger data protection policies. Ultimately, the review serves as a resource for those seeking to enhance the security of IoT devices and systems in an increasingly interconnected world.
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References
Y. Badr, X. Zhu, and M. N. Alraja, “Security and privacy in the Internet of Things: threats and challenges,” Serv. Oriented Comput. Appl., vol. 15, no. 4, pp. 257–271, Dec. 2021, doi: 10.1007/s11761-021-00327-z.
A. Sarango, G. Vásquez, and R. Torres, “A Vehicle-to-Vehicle Communication System using ZigBee,” IOP Conf. Ser. Earth Environ. Sci., vol. 1370, no. 1, pp. 012008, Jul. 2024, doi: 10.1088/1755-1315/1370/1/012008.
Md. Tauseef, M.R. Kounte, A.H. Nalband, and M. R. Ahmed, “Exploring the Joint Potential of Blockchain and AI for Securing Internet of Things,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 4, 2023, doi: 10.14569/IJACSA.2023.0140498.
H. Alrubayyi, G. Goteng, and M. Jaber, “AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities,” Network, vol. 3, no. 4, pp. 522–537, Nov. 2023, doi: 10.3390/network3040023.
H. Alrubayyi, G. Goteng, M. Jaber, and J. Kelly, “Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches,” J. Sens. Actuator Netw., vol. 10, no. 4, pp. 61, Oct. 2021, doi: 10.3390/jsan10040061.
C.A. Teodorescu, A.-N. Ciucu Durnoi, and V.M. Vargas, “The Rise of the Mobile Internet: Tracing the Evolution of Portable Devices,” Proc. Int. Conf. Bus. Excell., vol. 17, no. 1, pp. 1645–1654, Jul. 2023, doi: 10.2478/picbe- 2023-0147.
R. Rawat, “Harnessing the Power of IoT and AI for Human Evolution,” Int. J. Res. Sci. Eng., no. 33, pp. 58– 68, May 2023, doi: 10.55529/ijrise.33.58.68.
Z. Nie, Y. Long, S. Zhang, and Y. Lu, “A controllable privacy data transmission mechanism for Internet of things system based on blockchain,” Int. J. Distrib. Sens. Netw., vol. 18, no. 3, pp. 155013292210884, Mar. 2022, doi: 10.1177/15501329221088450.
N. Singh, R. Buyya, and H. Kim, “Securing Cloud-Based Internet of Things: Challenges and Mitigations,” Sensors, vol. 25, no. 1, pp. 79, Dec. 2024, doi: 10.3390/s25010079.
R. Mutha, R.R. Borhade, S.S. Barekar, S.S., Dari, D. Dhabliya, and M. Patil, “Enhancing Public Healthcare Security: Integrating Cutting-Edge Technologies into Social Medical Systems,” South East. Eur. J. Public Health, 2024, doi: 10.52710/seejph.483.
B. R. Kikissagbe and M. Adda, “Machine Learning-Based Intrusion Detection Methods in IoT Systems: A Comprehensive Review,” Electronics, vol. 13, no. 18, pp. 3601, Sep. 2024, doi: 10.3390/electronics13183601.
B. Susilo and R.F. Sari, “Intrusion Detection in IoT Networks Using Deep Learning Algorithm,” Information, vol. 11, no. 5, pp. 279, May 2020, doi: 10.3390/info11050279.
V. Dutta, M. Choras, M. Pawlicki, and R. Kozik, “A Deep Learning Ensemble for Network Anomaly and Cyber-Attack Detection,” Sensors, vol. 20, no. 16, pp. 4583, Aug. 2020, doi: 10.3390/s20164583.
H. Naeem, “Analysis of Network Security in IoT-based Cloud Computing Using Machine Learning: Analysis of Network Security in IoT-based Cloud Computing Using Machine Learning,” Int. J. Electron. Crime Investig., vol. 7, no. 2, Jul. 2023, doi: 10.54692/ijeci.2023.0702153.
H. Liu and B. Lang, “Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey,” Appl. Sci., vol. 9, no. 20, pp. 4396, Oct. 2019, doi: 10.3390/app9204396.
E. M. Campos et al., “Evaluating Federated Learning for intrusion detection in Internet of Things: Review and challenges,” Comput. Netw., vol. 203, pp. 108661, Feb. 2022, doi: 10.1016/j.comnet.2021.108661.
M. Prasad, P. Pal, S. Tripathi, and K. Dahal, “AI/ML driven intrusion detection framework for IoT-enabled cold storage monitoring system,” Security and Privacy, 7(5), e400, doi: 10.21203/rs.3.rs-2190363/v1.
M. Riegler, J. Sametinger, and M. Vierhauser, “A Distributed MAPE-K Framework for Self-Protective IoT Devices,” in 2023 IEEE/ACM 18th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), May 2023, pp. 202–208, doi: 10.1109/SEAMS59076.2023.00034.
V. Prakash, O. Odedina, A. Kumar, L. Garg, and S. Bawa, “A secure framework for the Internet of Things anomalies using machine learning,” Discover Internet of Things, vol. 4, no. 33, 2024, doi: 10.1007/s43926-024-00088-z.
D.A. Chaudhari, and E. Umamaheswari, “A new adaptive XOR, hashing and encryption-based authentication protocol for secure transmission of the medical data in Internet of Things (IoT),” Biomedical Engineering / Biomedizinische Technik, vol. 66, no. 1, pp. 91–105, Aug. 2020, doi: 10.1515/bmt-2019-0123.
Y.K. Beshah, S.L. Abebe, and H.M. Melaku, “Drift Adaptive Online DDoS Attack Detection Framework for IoT System,” Electronics, vol. 13, no. 6, pp. 1004, Mar. 2024, doi: 10.3390/electronics13061004.
P. Wu, C.-W. Chang, Y.-Y. Ma, and Z.-B. Zuo, “Constant-Size Credential-Based Packet Forwarding Verification in SDN,” Secur. Commun. Netw., vol. 2022, pp. 1–12, Oct. 2022, doi: 10.1155/2022/2270627.
S. Kavianpour, B. Shanmugam, S. Azam, M. Zamani, G. Narayana Samy, and F. De Boer, “A Systematic Literature Review of Authentication in Internet of Things for Heterogeneous Devices,” J. Comput. Netw. Commun., vol. 2019, pp. 1–14, Aug. 2019, doi: 10.1155/2019/5747136.
A. Rocha, H. Adeli, G. Dzemyda, F. Moreira, and A.M. Ramalho Correia, “Trends and Applications in Information Systems and Technologies,” Advances in Intelligent Systems and Computing, Vol. 1367, 2021, doi:10.1007/978-3-030-72660-7.
Y. Zheng, Y. Cao, and C.-H. Chang, “UDhashing: Physical Unclonable Function-Based User-Device Hash for Endpoint Authentication,” IEEE Trans. Ind. Electron., vol. 66, no. 12, pp. 9559–9570, Dec. 2019, doi: 10.1109/TIE.2019.2893831.
A. Babaei, and G. Schiele, “Physical Unclonable Functions in the Internet of Things: State of the Art and Open Challenges,” Sensors, vol. 19, no. 14, pp. 3208, Jul. 2019, doi: 10.3390/s19143208.
A. Golczynski, and J. A. Emanuello, “End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings,” arXiv. 2021, doi: 10.48550/ARXIV.2108.12276.
Z. Long, H. Yan, G. Shen, X. Zhang, H. He, and L. Cheng, “A Transformer-based network intrusion detection approach for cloud security,” J. Cloud Comput., vol. 13, no. 1, pp. 5, Jan. 2024, doi: 10.1186/s13677-023-00574-9.
M. Farooq, M. Khan, and R. A. Khan, “Graph-CNN Hybrid Advance Model for Accurate Anomaly Detection in Multivariate Time Series IoT Streams,” Feb. 28, 2024, doi: 10.21203/rs.3.rs-3977682/v1.
W.W. Lo, S. Layegby, M. Sarhan, M. Gallagher, and M. Portmann, “E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT,” in IEEE/IFIP Network Operations and Management Symposium (NOMS), 2022. doi: 10.1109/NOMS54207.2022.9789878.
A. Hangan, D. Lazea, and T. Cioara, “Privacy Preserving Anomaly Detection on Homomorphic Encrypted Data from IoT Sensors,”, arXiv preprint arXiv:2403.09322, Mar. 14, 2024
F. Sabrina, N. Li, and S. Sohail, “A Blockchain Based Secure IoT System Using Device Identity Management,” Sensors, vol. 22, no. 19, p. 7535, Oct. 2022, doi: 10.3390/s22197535.
T. Janssen, R. Berkvens, and M. Weyn, “Benchmarking RSS-based localization algorithms with LoRaWAN,” Internet of Things, vol. 12, pp. 100235, Dec. 2020, doi: 10.1016/j.iot.2020.100235.
F. Mazlan, N.F. Omar, N.N.M.S.N. Mohd, and A.A. Zainuddin, “Comprehensive insights into smart contracts: Architecture, sectoral applications, security analysis, and legal frameworks,” Journal of Informatics and Web Engineering, 4(1), pp. 1-17, 2025, doi: 10.33093/jiwe.2025.4.1.1.
M. H. Z. H. Nizam, M. A. A. Nizam, M. H. H. Jummadi, N. N. M. S. N. Mohd, and A.A. Zainuddin, “Hyperledger Fabric blockchain for securing the edge Internet of Things: A review,” Journal of Informatics and Web Engineering, 4(1), pp. 81-98, 2025, doi: 10.33093/jiwe.2025.4.1.7.
N. N. M. S. N. Mohd, S. Afiqah, S. Khadeja, A. Nasuha, W. M. H. N. I. Wan, M. Azman, ... and A.A. Zainuddin, “Surveys on the Security of Ethereum and Hyperledger Fabric Blockchain Platforms,” International Journal on Perceptive and Cognitive Computing, 11(1), pp. 16-40, 2025, doi: 10.31436/ijpcc.v11i1.526.