AI Article Synopsis
- Modern IoT systems require advanced security measures, and static analysis has proven effective in analyzing their software, but increasing complexity necessitates the automation of this process.
- A hypothesis is proposed suggesting that machine learning can enhance static analysis for IoT systems, leading to a structured research approach that confirms this applicability and establishes a framework.
- Key contributions include systemizing static analysis stages, formalizing machine-learning models, reviewing related publications, proving machine learning's utility at various analysis stages, and presenting an intelligent framework for IoT static analysis.
Article Abstract
Ensuring security for modern IoT systems requires the use of complex methods to analyze their software. One of the most in-demand methods that has repeatedly been proven to be effective is static analysis. However, the progressive complication of the connections in IoT systems, the increase in their scale, and the heterogeneity of elements requires the automation and intellectualization of manual experts' work. A hypothesis to this end is posed that assumes the applicability of machine-learning solutions for IoT system static analysis. A scheme of this research, which is aimed at confirming the hypothesis and reflecting the ontology of the study, is given. The main contributions to the work are as follows: systematization of static analysis stages for IoT systems and decisions of machine-learning problems in the form of formalized models; review of the entire subject area publications with analysis of the results; confirmation of the machine-learning instrumentaries applicability for each static analysis stage; and the proposal of an intelligent framework concept for the static analysis of IoT systems. The novelty of the results obtained is a consideration of the entire process of static analysis (from the beginning of IoT system research to the final delivery of the results), consideration of each stage from the entirely given set of machine-learning solutions perspective, as well as formalization of the stages and solutions in the form of "Form and Content" data transformations.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963110 | PMC |
| http://dx.doi.org/10.3390/s22041335 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Optimal life-cycle adaptation of coastal infrastructure under climate change.
Nat Commun
January 2025
Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA.
Climate change-related risk mitigation is typically addressed using cost-benefit analysis that evaluates mitigation strategies against a wide range of simulated scenarios and identifies a static policy to be implemented, without considering future observations. Due to the substantial uncertainties inherent in climate projections, this identified policy will likely be sub-optimal with respect to the actual climate trajectory that evolves in time. In this work, we thus formulate climate risk management as a dynamic decision-making problem based on Markov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs), taking real-time data into account for evaluating the evolving conditions and related model uncertainties, in order to select the best possible life-cycle actions in time, with global optimality guarantees for the formulated optimization problem.
View Article and Find Full Text PDFRural and remote health care: the case for spatial justice.
Rural Remote Health
January 2025
School of Health Sciences, Western Sydney University, Campbelltown, NSW 2560, Australia.
Almost universally, people living in rural and remote places die younger, poorer, and sicker than urban-dwelling citizens of the same country. Despite clear need, health services are commonly less available, and more costly and challenging to access, for rural and remote people. Rural geography is commonly cited as a reason for these disparities, that is, rural people are said to live in places too distant, too underpopulated, and too difficult to access.
View Article and Find Full Text PDFAssessment of simulation training efficacy in improving microsurgical skills: a retrospective analysis.
Int J Surg
January 2025
Department of Orthopaedics, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Background: Microsurgery demands an intensive period of skill acquisition due to its inherent complexity. The development and implementation of innovative training methods are essential for enhancing microsurgical outcomes. This study aimed to evaluate the impact of a simulation training program on the clinical results of fingertip replantation surgeries.
View Article and Find Full Text PDFMachine perfusion strategies in Liver Transplantation: A systematic review, pairwise, and network meta-analysis of randomized controlled trials.
Liver Transpl
January 2025
Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.
Background: Machine perfusion (MP), including hypothermic oxygenated machine perfusion (HOPE), dual HOPE, normothermic machine perfusion (NMP), NMP ischemia-free liver transplantation (NMP-ILT), and controlled oxygenated rewarming (COR), is increasingly being investigated to improve liver graft quality from extended criteria donors and donors after circulatory death and expand the donor pool. This network meta-analysis investigates the comparative efficacy and safety of various liver MP strategies versus traditional static cold storage (SCS).
Methods: We searched PubMed, Scopus, Web of Science, and Cochrane Controlled Register of Trials for randomized controlled trials (RCTs) comparing liver transplantation (LT) outcomes between SCS and MP techniques.
Relativistic and electron-correlation effects in static dipole polarizabilities for group 12 elements.
Phys Chem Chem Phys
January 2025
Institute of Applied Analysis and Numerical Simulation, University of Stuttgart, Pfaffenwaldring 57, Stuttgart, 70569, Germany.
In this study, we report a comprehensive calculation of the static dipole polarizabilities of group 12 elements using the finite-field approach combined with the relativistic coupled-cluster method, including single, double, and perturbative triple excitations. Relativistic effects are systematically investigated, including scalar-relativistic, spin-orbit coupling (SOC), and fully relativistic Dirac-Coulomb contributions. The final recommended polarizability values are 37.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!