2 research outputs found
A Review of Digital Twins and their Application in Cybersecurity based on Artificial Intelligence
The potential of digital twin technology is yet to be fully realized due to
its diversity and untapped potential. Digital twins enable systems' analysis,
design, optimization, and evolution to be performed digitally or in conjunction
with a cyber-physical approach to improve speed, accuracy, and efficiency over
traditional engineering methods. Industry 4.0, factories of the future, and
digital twins continue to benefit from the technology and provide enhanced
efficiency within existing systems. Due to the lack of information and security
standards associated with the transition to cyber digitization, cybercriminals
have been able to take advantage of the situation. Access to a digital twin of
a product or service is equivalent to threatening the entire collection. There
is a robust interaction between digital twins and artificial intelligence
tools, which leads to strong interaction between these technologies, so it can
be used to improve the cybersecurity of these digital platforms based on their
integration with these technologies. This study aims to investigate the role of
artificial intelligence in providing cybersecurity for digital twin versions of
various industries, as well as the risks associated with these versions. In
addition, this research serves as a road map for researchers and others
interested in cybersecurity and digital security.Comment: 60 pages, 8 Figures, 15 Table
A Comprehensive Survey of Imbalance Correction Techniques for Hyperspectral Data Classification
Land-cover classification is an important topic for remotely sensed hyperspectral (HS) data exploitation. In this regard, HS classifiers have to face important challenges, such as the high spectral redundancy, as well as noise, present in the data, and the fact that obtaining accurate labeled training data for supervised classification is expensive and time-consuming. As a result, the availability of large amounts of training samples, needed to alleviate the so-called Hughes phenomenon, is often unfeasible in practice. The class-imbalance problem, which results from the uneven distribution of labeled samples per class, is also a very challenging factor for HS classifiers. In this article, a comprehensive review of oversampling techniques is provided, which mitigate the aforementioned issues by generating new samples for the minority classes. More specifically, this article pursues a twofold objective. First, it reviews the most relevant oversampling methods that can be adopted according to the nature of HS data. Second, it provides a comprehensive experimental study and comparison, which are useful to derive practical conclusions about the performance of oversampling techniques in different HS image-based applications