Abstract

In recent years many of the applications have been migrated to the cloud mainly due to its flexibility and capability of cutting down on user Costs. But as we notice that many data are now being stored on the cloud there has been a rising concern on security. However, in a cloud environment, information, identity, database, application servers, and web servers are vulnerable to different types of cyber threats such as data breaches, identity theft, database manipulation, and more. Responses to these threats need sound investigation systems and methods hence giving rise to the concept of cloud forensics. Cloud forensics targets deal with cybercriminals in cloud infrastructures by presenting techniques for the detection, analysis, and prevention of illicit actions.

A significant component of this field is database forensics, which focuses on identifying and analyzing hostile actions performed by attackers on both the application layer and, behind the scenes. These activities are followed by physical data which are activity logs, database logs, and even network packets. Logs provide a great amount of information about the system and its interactions as well as possible irregularities. There is always additional information in the network that helps investigators locate the source of the attacks and study the actions of intruders.

To enhance the accuracy and efficiency of forensic investigation, new analytical methods may incorporate the use of Gaussian Membership Functions (GMFs). A comparison of the characteristics of GMFs revealed that they are developed from the Gaussian distribution and provide membership values to the elements in clusters according to their distances to the centers of the clusters. This viewpoint is used for those data sets, in which data distribution is matrix or even nearly normal like network packets or database query response time. Gaussian Membership Functions perform well in normal and outlier situations because they provide gradual rather than strict changes in membership values.

For instance, the use of GMFs in a cloud forensic investigation can pinpoint slight irregularities in traffic or database accesses which suggest that there has been a breach. Using such differences, forensic investigators can detect deviations that would not be apparent with other approaches because they are complex approaches. This capability is particularly important in recognizing various malicious activities in large-scale cloud environments, where the rate of data transactions and interactions is relatively high.

The inclusion of Gaussian Membership Functions in cloud forensics enhances the resilience against uncertainty and noise enabling a richer perspective of prospective threats. For this reason, GMFs are especially useful in discovering overlapping action patterns, such as the separation between normal, but anomalous user activity, and real attack footprints. Cloud forensics, thereby, utilizes such elaborate tools in a way that enables not only the identification and combating of cybercrimes but also the global mission of protecting the burgeoning sphere over the internet.

Using these techniques, the GMF is applied in analyzing the activity logs, database logs, and packets to show the techniques that promote cloud forensic investigations. This approach increases the effectiveness of recognizing and preventing cybertimes, protecting valuable information, and guaranteeing the security of cloud technologies. In particular, as cloud adoption progresses, the application of such progressive approaches will be critical in sustaining the confidence and safety of the underlying structures.

Citation

Saswati Chatterjee. “The Role of Cloud Forensics and Gaussian Membership Functions in Ensuring Data Security". Clareus Scientific Science and Engineering 2.2 (2025): 28-29.

Copyright

© 2025 Saswati Chatterjee. Licensee Clareus Scientific Publications. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.