Collaborative Anomaly Detection Framework for handling Big Data of Cloud Computing
This addresses security challenges in cloud computing for users and providers, but appears incremental as it builds on existing anomaly detection methods.
The paper tackles the problem of detecting cyber attacks in cloud computing environments by proposing a Collaborative Anomaly Detection Framework (CADF), which outperforms three state-of-the-art techniques in terms of false positive rate and detection rate on the UNSW-NB15 dataset.
With the ubiquitous computing of providing services and applications at anywhere and anytime, cloud computing is the best option as it offers flexible and pay-per-use based services to its customers. Nevertheless, security and privacy are the main challenges to its success due to its dynamic and distributed architecture, resulting in generating big data that should be carefully analysed for detecting network vulnerabilities. In this paper, we propose a Collaborative Anomaly Detection Framework CADF for detecting cyber attacks from cloud computing environments. We provide the technical functions and deployment of the framework to illustrate its methodology of implementation and installation. The framework is evaluated on the UNSW-NB15 dataset to check its credibility while deploying it in cloud computing environments. The experimental results showed that this framework can easily handle large-scale systems as its implementation requires only estimating statistical measures from network observations. Moreover, the evaluation performance of the framework outperforms three state-of-the-art techniques in terms of false positive rate and detection rate.