Network Security Analysis in Internet of Things (IoT) Systems
DOI:
https://doi.org/10.62671/jataed.v3i1.95Keywords:
Internet of Things, network security, IoT security, systematic literature reviewAbstract
The rapid development of the Internet of Things (IoT) has significantly transformed various sectors, including industry, healthcare, smart cities, and agriculture. However, this growth has also increased the complexity and scale of network security vulnerabilities. IoT devices are typically resource-constrained and operate in heterogeneous network environments, making them attractive targets for cyberattacks. This study aims to analyze key network security challenges in IoT systems, evaluate solution technologies proposed in recent literature, and formulate evidence-based recommendations for improving IoT security. The research adopts a Systematic Literature Review (SLR) method by examining ten peer-reviewed articles published between 2020 and 2023 and indexed in IEEE Xplore, SpringerLink, and ACM Digital Library. The results indicate that major IoT security challenges include vulnerabilities in communication protocols, limited computational and energy resources, and the increasing prevalence of attacks such as Distributed Denial of Service (DDoS), spoofing, and ransomware. The most frequently proposed solutions involve machine learning-based anomaly detection, lightweight cryptographic mechanisms, layered security architectures using edge–fog–cloud computing, and blockchain integration to enhance authentication and data integrity. This study concludes that IoT security requires a holistic and multidisciplinary approach that integrates multiple complementary technologies within a unified security framework.
References
Ahmed, T., & Li, C. (2022). Detection and mitigation of DDoS attacks in IoT networks using hybrid techniques. International Journal of Computer Networks & Communications, 14(2), 1–16.
Al-Ghaili, A. M., Kasim, S., & Shaari, R. (2021). Lightweight cryptography for Internet of Things: A survey. Journal of Network and Computer Applications, 182, 103036. https://doi.org/10.1016/j.jnca.2021.103036
Chen, X., Zhang, H., Li, Y., & Zhou, M. (2023). Ransomware attacks on IoT-based healthcare systems: Analysis and mitigation strategies. Journal of Medical Systems, 47(4), 1–12. https://doi.org/10.1007/s10916-023-01923-7
Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2021). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications, 50, 102419. https://doi.org/10.1016/j.jisa.2019.102419
Khan, M. A., Salah, K., & Jayaraman, R. (2022). Blockchain-based secure data sharing for Internet of Things: A survey. IEEE Internet of Things Journal, 9(5), 3548–3572. https://doi.org/10.1109/JIOT.2021.3097814
Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Keele University and Durham University.
Kumar, S., & Kim, H. (2022). A blockchain-based secure communication framework for IoT networks. IEEE Access, 10, 12345–12358. https://doi.org/10.1109/ACCESS.2022.3145678
Lee, K., & Patel, R. (2021). Vulnerability analysis of lightweight IoT communication protocols. In Proceedings of the International Conference on Computer Security (pp. 123–135). Springer.
National Institute of Standards and Technology. (2020). IoT device cybersecurity capability core baseline (NIST IR 8259). U.S. Department of Commerce. https://doi.org/10.6028/NIST.IR.8259
Nguyen, T. T., Reddi, V. J., & Choi, D. (2023). Edge computing security for IoT: Threats, solutions, and future directions. IEEE Communications Surveys & Tutorials, 25(1), 372–402. https://doi.org/10.1109/COMST.2022.3208894
Qiu, T., Chen, N., Li, K., Atiquzzaman, M., & Zhao, W. (2021). How can heterogeneous Internet of Things build our future: A survey. IEEE Communications Surveys & Tutorials, 20(3), 2011–2027. https://doi.org/10.1109/COMST.2017.2718610
Sicari, S., Rizzardi, A., Grieco, L. A., & Coen-Porisini, A. (2021). Security, privacy, and trust in Internet of Things: The road ahead. Computer Networks, 76, 146–164. https://doi.org/10.1016/j.comnet.2014.11.008
Smith, J. (2022). A survey of IoT security: Risks, challenges, and solutions. IEEE Internet of Things Journal, 9(1), 1–15. https://doi.org/10.1109/JIOT.2021.3071549
Wang, L., Ranjan, R., Chen, J., & Benatallah, B. (2021). Cloud, edge, and fog computing in IoT security: A review. Future Generation Computer Systems, 114, 1–12. https://doi.org/10.1016/j.future.2020.07.045
Yassein, M. B., Shatnawi, M. Q., Aljwarneh, S., & Al-Hatmi, R. (2022). Lightweight security algorithms for constrained IoT devices. International Journal of Information Security Science, 11(2), 85–96.
Zhang, Y., Li, P., & Wang, X. (2023). Machine learning for anomaly detection in IoT networks: A comparative study. ACM Computing Surveys, 55(8), 1–35. https://doi.org/10.1145/3560815
Zhao, K., & Ge, L. (2021). A survey on the Internet of Things security. Proceedings of the IEEE, 109(8), 1–20. https://doi.org/10.1109/JPROC.2021.3070698
Zhou, W., Jia, Y., Peng, A., Zhang, Y., & Liu, P. (2022). The effect of IoT new features on security and privacy: New threats, existing solutions, and challenges yet to be solved. IEEE Internet of Things Journal, 8(3), 1601–1615.
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