Machine learning to improve cloud storage security | News and current affairs

Machine learning to improve cloud storage security |  News and current affairs

Faced with the inexorable advance of new cyber threats, the security of data stored in the cloud has become one of the biggest concernsboth for suppliers and customers. The technologies applied to data protection are increasingly sophisticated, but they still need to improve to deal with the modern strategies and technologies of cybercriminals.

This is motivating the development of new strategies and technologies to protect cloud environments, as well as storage infrastructure, servers and networks, software and cloud-based services and applications. An example is the work carried out by a team of researchers from the Abdur Rahman Crescent Institute of Science and Technology in Chennai (India) and the Ingenium Research Group, from the University of Castilla-La Mancha, in Ciudad Real.

In the article published in the journal Springer Nature Switzerland AG, they explain that in cloud storage, files are stored in a remote database instead of being stored on a proprietary hard drive or on a local device. In these cloud-integrated environments the necessary data redundancy implies a great waste of capacity of storage, so there is a need to handle and dispose of redundant data more efficiently. This is complicated by implementing redundant data on top of encrypted data.

For this reason, these researchers have created non-redundant secure systems using the SHA-512 cryptographic algorithm in cloud environments. And they propose a model based on a Bayesian inference technique that addresses its two main objectives: minimizing redundant data and developing a strong encryption strategy to ensure safety.

According to the experiments they have carried out with this machine learning technique, the researchers have determined that cloud users are capable of make more effective use of cloud storage space, avoiding excess duplicate data and saving bandwidth. Although this method is much more demanding in terms of computational complexity, higher levels of security are achieved than systems commonly used in the industry.

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