Big data requires huge amounts of resources to analyze it for data driven decisions, thus there has been a gravitation towards cloud computing to work in this era of big data (Sakr, 2014). Cloud technology is different than personal systems that place different demands on cyber security, where personal systems could have single authority systems and cloud computing systems, have no individual owners, have multiple users, groups rights, and shared responsibility (Brookshear & Brylow, 2014; Prakash & Darbari, 2012). Cloud security can be just as good or better than personal systems because cloud providers could have the economies of scales that can support a budget to have an information security team that many organizations may not be able to afford (Connolly & Begg, 2014). Cloud security can be designed to be independently modular, which is great for heterogenous distributed systems (Prakash & Darbari, 2012).
For cloud computing eavesdropping, masquerading, message tampering, replaying the message, and denial of services are security issues that should be addressed (Prakash & Darbari, 2012). Sakr (2014) stated that exploitation of co-tenancy, a secure architecture for the cloud, accountability for outsourced data, confidentiality of data and computation, privacy, verifying outsourced computation, verifying capability, cloud forensics, misuse detection, and resource accounting and economic attacks are big issues for cloud security. This post will discuss the exploitation of co-tendency and confidentiality of data and computation.
Exploitation of Co-Tenancy: An issue with cloud security is within one of its properties, that it is a shared environment (Prakash & Darbari, 2012; Sakr, 2014). Given that it is a shared environment, people with malicious intent could pretend to be someone they are not to gain access, in other words masquerading (Prakash & Darbari, 2012). Once inside, these people with malicious intent tend to gather information about the cloud system and the data contained within it (Sakr, 2014). Another way these services could be used by malicious people is to use the computational resources of the cloud to carry out denial of service attacks on other people. Prakash and Darbari (2012) stated that two-factor authentications were used on personal devices and for shared distributed systems, there has been proposed a use of a three-factor authentication. The first two factors are the use passwords and smart cards. The last one could be either biometrics or digital certificates. Digital certificates can be used automatically to reduce end-user fatigue on using multiple authentications (Connolly & Begg, 2014). The third level of authentication helps to create a trusted system. Subsequently, a three-factor authentication could primarily mitigate masquerading. Sakr (2014), proposed using a tool that hides the IP addresses the infrastructure components that make up the cloud, to prevent the cloud for being used if the entry is granted to a malicious person.
Confidentiality of data and computation: If data in the cloud is accessed malicious people can gain information, and change the content of that information. Data stored on the distributed systems are sensitive to the owners of the data, like health care data which is heavily regulated for privacy (Sakr, 2014). Prakash and Darbari (2012) suggested the use of public key cryptography, software agents, XML binding technology, public key infrastructure, and role-based access control are used to deal with eavesdropping and message tampering. This essentially hides the data in such a way that it is hard to read without key items that are stored elsewhere in the cloud system. Sakr (2014) suggested homomorphic encryption may be needed, but warns that the use of encryption techniques increases the cost and time of performance. Finally, Lublinsky, Smith, and Yakubovich (2013), stated that encrypting the network to protect data-in-motion is needed.
Overall, a combination of data encryption, hiding IP addresses of computational components, and three-factor authentication may mitigate some of the cloud computing security concerns, like eavesdropping, masquerading, message tampering, and denial of services. However, using these techniques will increase the time it takes to process big data. Thus a cost-benefit analysis must be conducted to compare and contrast these methods while balancing data risk profiles and current risk models.
Resources:
- Brookshear, G., & Brylow, D. (2014). Computer Science: An Overview, (12th ed.). Pearson Learning Solutions. VitalBook file.
- Connolly, T., & Begg, C. (2014). Database Systems: A Practical Approach to Design, Implementation, and Management, 6th Edition. Pearson Learning Solutions. VitalBook file.
- Lublinsky, B., Smith, K., & Yakubovich, A. (2013). Professional Hadoop Solutions. Wrox. VitalBook file.
- Prakash, V., & Darbari, M. (2012). A review on security issues in distributed systems. International Journal of Scientific & Engineering Research, 3(9), 300–304.
- Sakr, S. (2014). Large scale and big data: Processing and management. Boca Raton, FL: CRC Press.
Dr. Skylar (Sky) Hernandez 