The open nature of Wireless Sensor Networks (WSNs) renders them an easy target to malicious code propagation, posing a significant and persistent threat to their security. Various mathematical models have been studied in recent literature for understanding the dynamics and control of the propagation of malicious codes in WSNs. However, due to the inherent randomness and uncertainty present in WSNs, stochastic modeling approach is essential for a comprehensive understanding of the propagation of malicious codes in WSNs. In this paper, we formulate a general stochastic compartmental model for analyzing the dynamics of malicious code distribution in WSNs and suggest its possible control. We incorporate the stochasticity in the classical deterministic model for the inherent unpredictability in code propagation, which results in a more appropriate representation of the dynamics. A basic theoretical analysis including the stability results of the model with randomness is carried out. Moreover, a higher-order spectral collocation technique is applied for the numerical solution of the proposed stochastic model. The accuracy and numerical stability of the model is presented. Finally, a comprehensive simulation is depicted to verify theoretical results and depict the impact of parameters on the model's dynamic behavior. This study incorporates stochasticity in a deterministic model of malicious codes spread in WSNs with the implementation of spectral numerical scheme which helps to capture these networks' inherent uncertainties and complex nature.
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http://dx.doi.org/10.1038/s41598-024-82033-2 | DOI Listing |
Sci Rep
January 2025
Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
The open nature of Wireless Sensor Networks (WSNs) renders them an easy target to malicious code propagation, posing a significant and persistent threat to their security. Various mathematical models have been studied in recent literature for understanding the dynamics and control of the propagation of malicious codes in WSNs. However, due to the inherent randomness and uncertainty present in WSNs, stochastic modeling approach is essential for a comprehensive understanding of the propagation of malicious codes in WSNs.
View Article and Find Full Text PDFFront Artif Intell
October 2024
Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, United States.
Life has become more comfortable in the era of advanced technology in this cutthroat competitive world. However, there are also emerging harmful technologies that pose a threat. Without a doubt, phishing is one of the rising concerns that leads to stealing vital information such as passwords, security codes, and personal data from any target node through communication hijacking techniques.
View Article and Find Full Text PDFComput Biol Med
December 2024
Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, Catania, 95126, Italy.
The progress in generative models, particularly Generative Adversarial Networks (GANs), opened new possibilities for image generation but raised concerns about potential malicious uses, especially in sensitive areas like medical imaging. This study introduces MITS-GAN, a novel approach to prevent tampering in medical images, with a specific focus on CT scans. The approach disrupts the output of the attacker's CT-GAN architecture by introducing finely tuned perturbations that are imperceptible to the human eye.
View Article and Find Full Text PDFJ Korean Med Sci
August 2024
Department of Radiation Oncology, Trakya University School of Medicine, Edirne, Türkiye.
The application of new technologies, such as artificial intelligence (AI), to science affects the way and methodology in which research is conducted. While the responsible use of AI brings many innovations and benefits to science and humanity, its unethical use poses a serious threat to scientific integrity and literature. Even in the absence of malicious use, the Chatbot output itself, as a software application based on AI, carries the risk of containing biases, distortions, irrelevancies, misrepresentations and plagiarism.
View Article and Find Full Text PDFOphthalmol Ther
October 2024
Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, 1000 Wall St, Rm 641, Ann Arbor, MI, 48105, USA.
Introduction: OpenAI recently introduced the ability to create custom generative pre-trained transformers (cGPTs) using text-based instruction and/or external documents using retrieval-augmented generation (RAG) architecture without coding knowledge. This study aimed to analyze the features of ophthalmology-related cGPTs and explore their potential utilities.
Methods: Data collection took place on January 20 and 21, 2024, and custom GPTs were found by entering ophthalmology keywords into the "Explore GPTS" section of the website.
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