Mixture Localization-Based Outliers Models for securing Data Migration in Cloud Centers

The cloud computing paradigm is changing how businesses operate, providing greater efficiency, tolerance, elasticity and flexibility in computing workloads. Underpinning these changes are multiple data centers, operated by different entities and distributed globally. Despite these benefits, cloud computing presents new classes of cyber-attack, opportunities to attack and processes to subvert. One of the primary strategies to defend against cyber-attacks is the migration process. A secure Virtual Machine (VM) migration is essential to safeguard cloud data centers against insider and outsider attacks. In this paper, we propose a collaborative anomaly detection system for discovering insider and outsider attacks from cloud systems and their migration process. The proposed system is named Mixture Localization-based Outliers (MLO) and utilizes Gaussian-mixture models for fitting network data and a local outlier factor function for discovering abnormal patterns in network traffic data. In order to validate the effectiveness of the models, the datasets of UNSW-NB15 and BoT-IoT are employed. The experimental results have revealed the high performance of the proposed system compared with several peer anomaly detection techniques.