Enhancing data security in cloud using random pattern fragmentation and a distributed nosql database

© 2019 IEEE. The cloud computing model has become very popular among users, as it has proven to be a cost-effective solution to store and process data, thanks to recent advancements in virtualization and distributed computing. Nevertheless, in the cloud environment, the user entrusts the safekeeping of its data entirely to the provider, which introduces the problem of how secure such data is and whether its integrity has been maintained. This paper proposes an approach to the data security in cloud by utilizing a random pattern fragmentation algorithm and combining it with a distributed NoSQL database. This not only increases the security of the data by storing it in different nodes and scramble all the bytes, but also allows the user to implement an alternative method of securing data. The performance of the approach is compared to other approaches, along with AES 256 encryption. Results indicate a significant performance improvement over encryption, highlighting the capabilities of this method for cloud stored data, as it creates a layer of protection without additional overhead

Big data security on cloud servers using data fragmentation technique and NoSQL database

© Springer International Publishing AG, part of Springer Nature 2019. Cloud computing has become so popular that most sensitive data are hosted on the cloud. This fast-growing paradigm has brought along many problems, including the security and integrity of the data, where users rely entirely on the providers to secure their data. This paper investigates the use of the pattern fragmentation to split data into chunks before storing it in the cloud, by comparing the performance on two different cloud providers. In addition, it proposes a novel approach combining a pattern fragmentation technique with a NoSQL database, to organize and manage the chunks. Our research has indicated that there is a trade-off on the performance when using a database. Any slight difference on a big data environment is always important, however, this cost is compensated by having the data organized and managed. The use of random pattern fragmentation has great potential, as it adds a layer of protection on the data without using as much resources, contrary to using encryption

Extrusion detection of illegal files in cloud-based systems

Cloud-based architectures have become the predominant paradigm for organisational infrastructure development due to the flexibility and scalability that these systems provide. However, issues around privacy and trust in such environments remain as has been demonstrated in recent attacks. There are two security challenges for cloud providers to resolve. First, they must ensure that only authorised downloads of potentially sensitive data can be made and they should have a means by which to detect any malicious activities. Second, any files that are uploaded to cloud providers must adhere to geographical legalities. Current security mechanisms employed in the cloud, such as firewalls and Intrusion Detection Systems, find these issues problematic. This paper therefore presents a novel approach, XDet, for the extrusion detection of illegal files being maliciously uploaded to or downloaded from the cloud, which can be used in conjunction with other security countermeasures to ensure robust and secure cloud systems. This is achieved through the creation and detection of signatures from files of interest within the cloud network environment. The feasibility and performance study in this paper, whereby XDet has been applied to network traffic to detect files of interest, demonstrates the applicability of this approach