Network security is a crucial challenge facing Internet-of-Things (IoT) systems worldwide, which leads to serious safety alarms and great economic loss. This paper studies the problem of malicious interdicting network exploitation of IoT systems that are modeled as a bi-layer logical-physical network. In this problem, a virtual attack takes place at the logical layer (the layer of Things), while the physical layer (the layer of Internet) provides concrete support for the attack. In the interdiction problem, the attacker attempts to access a target node on the logical layer with minimal communication cost, but the defender can strategically interdict some key edges on the physical layer given a certain budget of interdiction resources. This setting generalizes the classic single-layer shortest-path network interdiction problem, but brings in nonlinear objective functions, which are notoriously challenging to optimize. We reformulate the model and apply Benders decomposition process to solve this problem. A layer-mapping module is introduced to improve the decomposition algorithm and a random-search process is proposed to accelerate the convergence. Extensive numerical experiments demonstrate the computational efficiency of our methods.
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http://dx.doi.org/10.3390/s20205943 | DOI Listing |
RSC Adv
January 2025
College of Agriculture and Biological Science, Dali University Dali 671000 China
The conformational dynamics and activation mechanisms of KRAS proteins are of great importance for targeted cancer therapy. However, the detailed molecular mechanics of KRAS activation induced by GTP binding remains unclear. In this study, we systematically investigated how GTP/GDP exchange affects the thermodynamic and kinetic properties of KRAS and explored the activation mechanism using molecular dynamics (MD) simulations, Markov state models (MSMs), and neural relational inference (NRI) models.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Theory and Simulation of Complex Systems, Institute of Physical Chemistry, Heinrich-Heine Universität, Universitätsstr. 1, 40225 Düsseldorf, Germany.
Understanding and analyzing large-scale reaction networks is a fundamental challenge due to their complexity and size, often beyond human comprehension. In this paper, we introduce AUTOGRAPH, the first web-based tool designed for the interactive three-dimensional (3D) visualization and construction of reaction networks. AUTOGRAPH emphasizes ease of use, allowing users to intuitively build, modify, and explore individual reaction networks in real time.
View Article and Find Full Text PDFIISE Trans Occup Ergon Hum Factors
January 2025
The Polytechnic School, Arizona State University, Mesa, AZ, USA.
OCCUPATIONAL APPLICATIONSResults from our exploratory study of restaurant worker mental models of injury and safety emphasize the need for improved occupational safety in the culinary industry through targeted interventions for chefs and managers. The analysis we performed showed that managers possess more integrated and coherent mental models of injury and safety than chefs, reflected in network parameters showing better organization of safety concepts. Kitchen training programs should focus on bridging gaps in safety awareness and mitigating hazards such as burns, cuts, slips, and equipment-related risks.
View Article and Find Full Text PDFAdv Mater
December 2024
Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea.
Graph theory has been widely used to quantitatively analyze complex networks of molecules, materials, and cells. Analyzing the dynamic complex structure of extracellular matrix can predict cell-material interactions but has not yet been demonstrated. In this study, graph theory-based mathematical modeling of RGD ligand graph inter-relation is demonstrated by differentially cutting off RGD-to-RGD interlinkages with flexibly conjugated magnetic nanobars (MNBs) with tunable aspect ratio.
View Article and Find Full Text PDFCogn Neurodyn
December 2024
Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Deception detection is a critical aspect across various domains. Integrating advanced signal processing techniques, particularly in neuroscientific studies, has opened new avenues for exploring deception at a deeper level. This study uses electroencephalogram (EEG) signals from a balanced cohort of 22 participants, consisting of both males and females, aged between 22 and 29, engaged in a visual task for instructed deception.
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