Deep Guard-IoT: A Systematic Review of AI-Based Anomaly Detection Frameworks for Next-Generation IoT Security (2020-2024)

Asst. Prof. Alaa Abdul HUSSAIN Dleih Almowsawi

doi:10.31185/wjps.598

Authors

Asst. Prof. Alaa Abdul HUSSAIN Dleih Almowsawi College of Education for Pure Science, Computer Science dept. Wasit University

DOI:

https://doi.org/10.31185/wjps.598

Keywords:

Anomaly Detection, Artificial Intelligence , Cybersecurity

Abstract

The emergence of IoT devices has complicated the landscape of cybersecurity in ways that had never been experienced before, thereby, giving raise to the need for more developed methods that can assist in threat evaluation and deterrent. The work in the international scope analyses the particulars of the artificial intelligence-based anomaly detection in the IoT technology implementation including the perspectives of recent period between 2020 and 2024 of the various architectures’ effectiveness and their use in low-resource IoT environments. In this review, as well as in many other recent works carried out by other authors, it is observed that deep learning methods, such us Long Short-Term Memory (LSTM) networks and GRU-LSTM hybrid models, achieved the most accurate performance, ranging from 96% up to 99.9% correct detection. Our examination focuses on several aspects of IoT security, such as challenges at the device level, issues related to security at the network level, data security, and facets related to artificial intelligence concepts and architectures capable of addressing the challenges. The results of the study state that the AI techniques tend to be more efficient and have a high performance than the conventional methods before but have drawbacks for realistic application owing to lack of adequate resources, absence of standard practices and sophistication of new threats. The study also highlights the gaps that exist in addressing the current approaches and makes suggestions on what is to be done, such as the importance of developing

References

H. Allioui and Y. Mourdi, “Exploring the full potentials of IoT for better financial growth and stability: A comprehensive survey,” Sensors, vol. 23, no. 19, p. 8015, 2023.

T. Payton and T. Claypoole, Privacy in the age of Big data: Recognizing threats, defending your rights, and protecting your family. Rowman & Littlefield, 2023.

S. Ahmed and M. Khan, “Securing the Internet of Things (IoT): A comprehensive study on the intersection of cybersecurity, privacy, and connectivity in the IoT ecosystem,” AI, IoT Fourth Ind. Revolut. Rev., vol. 13, no. 9, pp. 1–17, 2023.

B. Nawaal, U. Haider, I. U. Khan, and M. Fayaz, “Signature-Based Intrusion Detection System for IoT,” in Cyber Security for Next-Generation Computing Technologies, CRC Press, 2024, pp. 141–158.

Y. Otoum, N. Gottimukkala, N. Kumar, and A. Nayak, “Machine Learning in Metaverse Security: Current Solutions and Future Challenges,” ACM Comput. Surv., vol. 56, no. 8, pp. 1–36, 2024.

B. Alotaibi, “A survey on industrial Internet of Things security: Requirements, attacks, AI-based solutions, and edge computing opportunities,” Sensors, vol. 23, no. 17, p. 7470, 2023.

S. Saurabh and P. K. Gupta, “Deep learning-based modified bidirectional LSTM network for classification of ADHD disorder,” Arab. J. Sci. Eng., vol. 49, no. 3, pp. 3009–3026, 2024.

M. S. Al-kahtani, Z. Mehmood, T. Sadad, I. Zada, G. Ali, and M. ElAffendi, “Intrusion detection in the Internet of Things using fusion of GRU-LSTM deep learning model,” Intell. Autom. Soft Comput., vol. 37, no. 2, 2023.

K. O. Adefemi Alimi, K. Ouahada, A. M. Abu-Mahfouz, S. Rimer, and O. A. Alimi, “Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things,” J. Sens. Actuator Networks, vol. 11, no. 3, p. 32, Jul. 2022, doi: 10.3390/jsan11030032.

B. Wang, Z. Chang, S. Li, and T. Hämäläinen, “An efficient and privacy-preserving blockchain-based authentication scheme for low earth orbit satellite-assisted internet of things,” IEEE Trans. Aerosp. Electron. Syst., vol. 58, no. 6, pp. 5153–5164, 2022.

R. Rani, M. Khurana, A. Kumar, and N. Kumar, “Big data dimensionality reduction techniques in IoT: Review, applications and open research challenges,” Cluster Comput., vol. 25, no. 6, pp. 4027–4049, 2022.

S. Shende and S. Thorat, “Long short-term memory (LSTM) deep learning method for intrusion detection in network security,” Int. J. Eng. Res., vol. 9, no. 06, 2020.

Z. Li and A. Rainer, “Reproducible Searches in Systematic Reviews: An Evaluation and Guidelines,” IEEE Access, 2023.

J. S. Barrot, “Trends in automated writing evaluation systems research for teaching, learning, and assessment: A bibliometric analysis,” Educ. Inf. Technol., vol. 29, no. 6, pp. 7155–7179, 2024.

P. I. R. Grammatikis, P. G. Sarigiannidis, and I. D. Moscholios, “Securing the Internet of Things: Challenges, threats and solutions,” Internet of Things, vol. 5, pp. 41–70, 2019.

W. Iqbal, H. Abbas, M. Daneshmand, B. Rauf, and Y. A. Bangash, “An in-depth analysis of IoT security requirements, challenges, and their countermeasures via software-defined security,” IEEE Internet Things J., vol. 7, no. 10, pp. 10250–10276, 2020.

S. N. Swamy and S. R. Kota, “An empirical study on system level aspects of Internet of Things (IoT),” IEEE Access, vol. 8, pp. 188082–188134, 2020.

F. J. Jaime, A. Muñoz, F. Rodríguez-Gómez, and A. Jerez-Calero, “Strengthening privacy and data security in biomedical microelectromechanical systems by IoT communication security and protection in smart healthcare,” Sensors, vol. 23, no. 21, p. 8944, 2023.

M. O. Akinsanya, C. C. Ekechi, and C. D. Okeke, “Security paradigms for iot in telecom networks: conceptual challenges and solution pathways,” Eng. Sci. Technol. J., vol. 5, no. 4, pp. 1431–1451, 2024.

A. Joseph, “A Holistic Framework for Unifying Data Security and Management in Modern Enterprises,” Int. J. Soc. Bus. Sci., vol. 17, no. 10, pp. 602–609, 2023.