Anomaly detection in cloud network data

Cloud computing is one of the most rapidly expanding computing concepts in the modern IT world. Cloud computing interconnects data and applications served from multiple geographic locations. A large number of transactions and the hidden infrastructure in cloud computing systems have presented a number of challenges to the research community. Among them, maintaining the cloud network security has become a key challenge. For example, detecting anomalous data has been a key research area in cloud computing. Anomaly detection (or outlier detection) is the identification of suspicious or uncommon data that significantly differs from the majority of the data. Recently, machine learning methods have shown their effectiveness in anomaly detection. However, identifying anomalies or outliers using supervised learning methods still a challenging task due to the class imbalance and the unpredictable nature and inconsistent properties or patterns of anomaly data. One-class classifiers are one feasible solution for this issue. In this paper, we mainly focused on analyzing cloud network data for identifying anomalies using one-class classification methods namely One Class Support Vector Machine (OCSVM) and Autoencoder. Here, we used a benchmark data set, YAHOO Synthetic cloud network data set. To the best of our knowledge, this is the first study that used YAHOO data for detecting anomalies. According to our analysis, Autoencoder achieves 96.02 percent accuracy in detecting outliers and OCSVM achieves 79.05 percent accuracy. In addition, we further investigated the effectiveness of a one class classification method using another benchmarked data set, UNSW-NB15. There we obtained 99.10 percent accuracy for Autoencoder and 60.89 percent accuracy for OCSVM. The above results show the neural network-based methods perform better than the kernel-based methods in anomaly detection in cloud network data.