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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 12, 2023.
Abstract: Anomaly detection plays a crucial role in ensuring the security and integrity of Internet of Things (IoT) surveillance systems. Nowadays, deep learning methods have gained significant popularity in anomaly detection because of their ability to learn and extract intricate features from complex data automatically. However, despite the advancements in deep learning-based anomaly detection, several limitations and research gaps exist. These include the need for improving the interpretability of deep learning models, addressing the challenges of limited training data, handling concept drift in evolving IoT environments, and achieving real-time performance. It is crucial to conduct a comprehensive review of existing deep learning methods to address these limitations as well as identify the most accurate and effective approaches for anomaly detection in IoT surveillance systems. This review paper presents an extensive analysis of existing deep learning methods by collecting results and performance evaluations from various studies. The collected results enable the identification and comparison of the most accurate deep-learning methods for anomaly detection. Finally, the findings of this review will contribute to the development of more efficient and reliable anomaly detection techniques for enhancing the security and effectiveness of IoT surveillance systems.
Jianchang HUANG, Yakun CAI and Tingting SUN, “Investigating of Deep Learning-based Approaches for Anomaly Detection in IoT Surveillance Systems” International Journal of Advanced Computer Science and Applications(IJACSA), 14(12), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0141279
@article{HUANG2023,
title = {Investigating of Deep Learning-based Approaches for Anomaly Detection in IoT Surveillance Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0141279},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0141279},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {12},
author = {Jianchang HUANG and Yakun CAI and Tingting SUN}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.