Enhancing Cloud Network Security with Innovative Time Series Analysis

Cloud computing has revolutionized computing infrastructure abstraction and utilization, distinguished by its cost-effective and high-quality services. However, the challenge of securing cloud networks persists, mainly due to the broad exchange of data and the inherent complexity of these techniques. Anomaly detection emerges as a promising solution to improve cloud network safeness, presenting perception into system behavior and alerting operators for further actions. This paper offers a novel time series analysis method for detecting anomalies in cloud networks. Our technique employs innovative time series analysis techniques based on a matrix profile, and the Kneedle algorithm to identify multi-dimensional anomalous patterns within multiple features extracted from network traffic streams. To evaluate the efficacy of our approach, we implemented timestamp-based and index-based methods to two distinct datasets: the most widely used UNSW-NB15 and the recently introduced CICIoT2023 datasets. The results highlight the efficacy of our proposed method in identifying cloud network anomalies. It achieved an impressive accuracy of 99.6% and an F1-score of 99.8% using the timestamp-based analysis method. For the index-based analysis method, accuracy reached 98%, accompanied by an outstanding F1-score of 99.9%.