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@article{194521,
        author = {D. Fernandez Raj and Dr B V V Siva Prasad},
        title = {Integrative Insights into Hybrid CNN–Autoencoder Models for Security Threat Detection in Cloud Environments},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {4137-4149},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194521},
        abstract = {This synthesizing paper summarizes the conclusions of the four previous papers that look at the Hybrid CNN -Autoencoders to detect security threats in clouds. With the gradual but steady increase in scale and complexity of cloud computing emergence, traditional intrusion detectors systems (IDS) are becoming unsuitable in the face of the advanced and dynamic character of cyber threats. The papers collectively examine different areas of Hybrid CNN -Autoencoder model such as its performance, optimization approaches, comparative analysis, and the architectural mechanisms that have been employed in the effective identification of known and unknown threats. This hybrid model is much better in detection accuracy, computational efficiency, and scalability than the traditional machine learning (ML) and deep learning (DL) models because it utilizes the Convolutional Neural Networks (CNNs) to extract the features and the Autoencoders to detect the anomalies. These techniques are combined in a manner that enables the model to efficiently model the spatial relationship of network traffic and log data as well as detect new attack patterns via the reconstruction error of the Autoencoder. These papers have been synthesized to show that the model is viable in real time cloud security applications. These and many other optimization methods, including hyperparameter tuning, feature selection, and model regularization, are also covered in the study and contribute to making it more efficient and consume fewer resources, thus it is applicable in resource-constrained cloud environments. Moreover, the paper discusses the implication of these findings, which can be of great importance in the future research context and practical implementation of such systems to achieve strong cloud security. The findings of the synthesis indicate that the Hybrid CNN-Autoencoder model is a prospective solution to such dynamic and complex security issues of the cloud environment, where both high-detection rates and low-computation rates are provided. Future work suggestions are to broaden the capabilities of the model in order to deal with a broader variety of attack vectors, to optimize it more in real-time cloud environments, and to investigate how to make use of it with edge computing and low-power devices.},
        keywords = {Hybrid CNN–Autoencoder, Cloud Security, Intrusion Detection, Anomaly Detection, Feature Extraction, Deep Learning, CNN, Autoencoder.},
        month = {March},
        }