| Title | Machine Learning-Based Anomalies Detection in Cloud Virtual Machine Resource Usage |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Ntambu, Peter, Adeshina, Steve A |
| Conference Name | 2021 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS) |
| Keywords | anomaly detection, cloud computing, Cloud Security, composability, cryptography, cyber physical systems, Forestry, machine learning, machine learning algorithms, Operating systems, pubcrawl, resilience, Resiliency, Support vector machines, Technological innovation, Time series analysis, Virtual machine resource, virtual machine security, virtualization |
| Abstract | Cloud computing is one of the greatest innovations and emerging technologies of the century. It incorporates networks, databases, operating systems, and virtualization technologies thereby bringing the security challenges associated with these technologies. Security Measures such as two-factor authentication, intrusion detection systems, and data backup are already in place to handle most of the security threats and vulnerabilities associated with these technologies but there are still other threats that may not be easily detected. Such a threat is a malicious user gaining access to the Virtual Machines (VMs) of other genuine users and using the Virtual Machine resources for their benefits without the knowledge of the user or the cloud service provider. This research proposes a model for proactive monitoring and detection of anomalies in VM resource usage. The proposed model can detect and pinpoint the time such anomaly occurred. Isolation Forest and One-Class Support Vector Machine (OCSVM) machine learning algorithms were used to train and test the model on sampled virtual machine workload trace using a combination of VM resource metrics together. OCSVM recorded an average F1-score of 0.97 and 0.89 for hourly and daily time series respectively while Isolation Forest has an average of 0.93 and 0.80 for hourly and daily time series. This result shows that both algorithms work for the model however OCSVM had a higher classification success rate than Isolation Forest. |
| DOI | 10.1109/ICMEAS52683.2021.9692308 |
| Citation Key | ntambu_machine_2021 |
Machine Learning-Based Anomalies Detection in Cloud Virtual Machine Resource Usage