Biblio

Virtual worlds was becoming increasingly popular in a variety of fields, including education, business, space exploration, and video games. Establishing the security of virtual worlds was becoming more critical as they become more widely used. Virtual users were identified using a behavioral biometric system. Improve the system's ability to identify objects by fusing scores from multiple sources. Identification was based on a review of user interactions in virtual environments and a comparison with previous recordings in the database. For behavioral biometric systems like the one described, it appears that score-level biometric fusion was a promising tool for improving system performance. As virtual worlds become more immersive, more people will want to participate in them, and more people will want to be able to interact with each other. Each region of the Meta-verse was given a glimpse of the current state of affairs and the trends to come. As hardware performance and institutional and public interest continue to improve, the Meta-verse's development is hampered by limitations like computational method limits and a lack of realized collaboration between virtual world stakeholders and developers alike. A major goal of the proposed research was to verify the accuracy of the biometric system to enhance the security in virtual world. In this study, the precision of the proposed work was compared to that of previous work.

While the existence of many security elements in software can be measured (e.g., vulnerabilities, security controls, or privacy controls), it is challenging to measure their relative security impact. In the physical world we can often measure the impact of individual elements to a system. However, in cyber security we often lack ground truth (i.e., the ability to directly measure significance). In this work we propose to solve this by leveraging human expert opinion to provide ground truth. Experts are iteratively asked to compare pairs of security elements to determine their relative significance. On the back end our knowledge encoding tool performs a form of binary insertion sort on a set of security elements using each expert as an oracle for the element comparisons. The tool not only sorts the elements (note that equality may be permitted), but it also records the strength or degree of each relationship. The output is a directed acyclic ‘constraint’ graph that provides a total ordering among the sets of equivalent elements. Multiple constraint graphs are then unified together to form a single graph that is used to generate a scoring or prioritization system.For our empirical study, we apply this domain-agnostic measurement approach to generate scoring/prioritization systems in the areas of vulnerability scoring, privacy control prioritization, and cyber security control evaluation.

The development of marine environment monitoring equipment has been improved by leaps and bounds in recent years. Numerous types of marine environment monitoring equipment have mushroomed with a wide range of high-performance capabilities. However, the existing data recording software cannot meet the demands of real-time and comprehensive data recording in view of the growing data types and the exponential data growth rate generated by various types of marine environment monitoring equipment. Based on the above-mentioned conundrum, this paper proposes a multi-source heterogeneous marine environmental data acquisition and storage method, which can record and replay multi-source heterogeneous data based upon the needs of real-time and accurate performance and also possess good compatibility and expandability.

Rapid advancement in wearable technology has unlocked a tremendous potential of its applications in the medical domain. Among the challenges in making the technology more useful for medical purposes is the lack of confidence in the data thus generated and communicated. Incentives have led to attacks on such systems. We propose a novel lightweight scheme to securely log the data from bodyworn sensing devices by utilizing neighboring devices as witnesses who store the fingerprints of data in Bloom filters to be later used for forensics. Medical data from each sensor is stored at various locations of the system in chronological epoch-level blocks chained together, similar to the blockchain. Besides secure logging, the scheme offers to secure other contextual information such as localization and timestamping. We prove the effectiveness of the scheme through experimental results. We define performance parameters of our scheme and quantify their cost benefit trade-offs through simulation.