SECURE KUBERNETES RESOURCES FROM CPU BASED CRYPTOJACKING

Abstract

This research is being conducted to understand the behaviour of the cloud application deployed in the public cloud, private cloud and Blockchain based Hyperledger fabric environments. The main idea is to extract the intension of the cloud resource usage by the application in the generous manner and to derive the expected results that is intended to perform.
Due to the growing demand of the Cryptocurrency usage, the environments used to mine the cryptocurrency is drawing a lot of attention. The environment that is in the form of cloud resources, on prem resources pose a lot of security threats from the hacker in utilizing the hardware resources without the application being disturbed. This results in the increased cost cloud resources billed to the customer.
The tools used in the research methodology to detect the cryptojacking comprises of the open source (Intel V-Tune) and not bind to any hardware vendor that cater the needs of the customer. The goal is to analyse the application on the fly using the tool and collect the data from output of the tools. The data is further cleaned and converted to format (CSV) that is consumed by the necessary deciding logic to further analyse.
The source of the data is driven by the open-source tools used for mining the cryptocurrency. The mining can be done on any popular cloud agonistic platforms that has well defined hardware config. After the application is found to be using the excess hardware that is beyond the threshold value, the method of finding the application usage is done by the method described above.
The challenge in K8S environment to bring the application down without interrupting the operation is well compensated by the ISTIO mesh framework. ISTIO helps in diverting the user traffic for the application to the other instance of the application on the fly. The side car container injection to the K8S pods and configuring the destination rules makes the process smooth in bringing down the affected application.
The results of the study were conducted on the industry standard hardware such has Intel and AMD CPU’s. The MSR (Model specific register) configuration plays a vital role in identifying the affected application before and after the application starts.
This research helps the academic people to further enhance the study of different mining application and helps the Enterprise sector to reduce the TCO (Total Cost of Ownership) by identifying the unintended usage of hardware resource by the application.
The results obtained from the research shows that for identifying the cryptojacking the tools used shows positive note. The usage of the tools along with the ease of configuration with respect to the customer application gains upper hand. In short context, the open-source tools selection with hardware platform as abstract on which customer has chosen for the application deployment helps to reduce the TCO.
The results of the study were compared to those of Rupesh Raj Karn et al (2021). The findings of this study may be used by existing academic as a reference in further enhancing the methods of identifying the cryptojacking.