Citation:

Gaurav Sarraf, Vibhor Pal, 2024. "Adaptive Deep Learning for Identification of Real-Time Anomaly in Zero-Trust Cloud Networks", ESP Journal of Engineering & Technology Advancements  4(3): 209-218.

Abstract:

The zero-trust cloud networks decentralized and dynamic nature turns them into easy targets of advanced cyberattacks, and the traditional security controls are not sufficient anymore. This article introduces a self-sovereign cloud security model utilizing Convolutional Neural Network (CNN) to detect anomalies. Selected methodology utilizes CSE-CIC-IDS2018 dataset, and includes extensive preprocessing in terms of outlier elimination by Local Outlier Factor (LOF), Z-score outliers, and dimensionality reduction by Principal Component Analysis (PCA). The dataset is split into testing of 10% and 90% training data and CNN model is used to extract spatio-temporal patterns of network traffic to classify normal and malicious streams with high accuracy. It is shown through experimental findings which proposed model has a higher performance of 99.87 accuracy, 99.86% recall and precision, and a F1-score of 99.87, being better than benchmark models like the MLP-PSO, LSTM, and SVM. The findings demonstrate the strength, scalability, and usefulness of the model in offering real-time Anomaly Detection (AD) in zero-trust cloud networks.

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Keywords:

Zero-Trust Cloud Networks, Anomaly Detection, Local Outlier Factor (LOF), Patio-Temporal Patterns, Intrusion Detection, Cybersecurity, Machine Learning (ML), Deep Learning (DL).