Enhancing Cloud Network Security with Innovative Time Series Analysis

Abstract Cloud computing has revolutionized computing infrastructure abstraction and utilisation, characterized by its cost-effective and high-quality services. However, the challenge of securing cloud networks persists, primarily due to the extensive exchange of data and the inherent complexity of these systems. Anomaly detection emerges as a promising solution to enhance cloud network security, offering insights into system behaviour and alerting operators for further actions. This paper presents a novel time series analysis technique for detecting anomalies in cloud networks. Our approach utilises a multi-dimensional matrix profile, an innovative time series analysis method, to highlight anomalous patterns within multiple features extracted from network traffic streams. Additionally, we employ the Kneedle algorithm to pinpoint the highlighted patterns that identify anomalies. To evaluate the effectiveness of our method, 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, the accuracy reached 98%, accompanied by an outstanding F1-score of 99.9%.