Enhancing Intrusion Detection Using Deep Neural Networks in Cloud Environments

Biyyala Swathi; Allam Balaram; Ajmeera Kiran; V Thrimurthulu

doi:10.36948/ijfmr.2025.v07i05.55919

Enhancing Intrusion Detection Using Deep Neural Networks in Cloud Environments

Author(s)	Ms. Biyyala Swathi, Dr. Allam Balaram, Dr. Ajmeera Kiran, Dr. V Thrimurthulu
Country	India
Abstract	Intrusion Detection Systems (IDS) are vital for securing cloud environments, where dynamic scalability and distributed architecture present significant security challenges. Accurate, real-time threat detection with minimal false positives is essential for effective cloud protection. Traditional machine learning models such as Support Vector Machines (SVM) and Random Forests have been applied in IDS but often struggle with high false positive rates and limited effectiveness against novel or zero-day attacks. A deep neural network (DNN) architecture is designed, incorporating multiple ReLU-activated hidden layers and dropout layers to prevent overfitting. The model is trained using the Adam optimizer and categorical cross-entropy loss to ensure efficient learning and responsiveness. Evaluation on NSL-KDD and CICIDS2017 datasets demonstrates high detection accuracy—98.7% and 97.4% respectively—along with reduced false positive rates and promising results in identifying zero-day attacks during simulation. Future work includes integrating federated learning to support distributed deployment and enhance privacy preservation.
Keywords	Intrusion Detection Systems (IDS), Deep Neural Networks (DNNs), Cloud Security, Detection Accuracy and False Positive Reduction
Field	Engineering
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-09-16
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.55919

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Enhancing Intrusion Detection Using Deep Neural Networks in Cloud Environments

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