Human-Unrecognizable Differential Private Noised Image Generation Method.

Differential privacy has emerged as a practical technique for privacy-preserving deep learning. However, recent studies on privacy attacks have demonstrated vulnerabilities in the existing differential privacy implementations for deep models. While encryption-based methods offer robust security, their computational overheads are often prohibitive. To address these challenges, we propose a novel differential privacy-based image generation method. Our approach employs two distinct noise types: one makes the image unrecognizable to humans, preserving privacy during transmission, while the other maintains features essential for machine learning analysis. This allows the deep learning service to provide accurate results, without compromising data privacy. We demonstrate the feasibility of our method on the CIFAR100 dataset, which offers a realistic complexity for evaluation.

Bypassing Heaven's Gate Technique Using Black-Box Testing.

In recent years, the number and sophistication of malware attacks on computer systems have increased significantly. One technique employed by malware authors to evade detection and analysis, known as Heaven's Gate, enables 64-bit code to run within a 32-bit process. Heaven's Gate exploits a feature in the operating system that allows the transition from a 32-bit mode to a 64-bit mode during execution, enabling the malware to evade detection by security software designed to monitor only 32-bit processes. Heaven's Gate poses significant challenges for existing security tools, including dynamic binary instrumentation (DBI) tools, widely used for program analysis, unpacking, and de-virtualization. In this paper, we provide a comprehensive analysis of the Heaven's Gate technique. We also propose a novel approach to bypass the Heaven's Gate technique using black-box testing. Our experimental results show that the proposed approach effectively bypasses and prevents the Heaven's Gate technique and strengthens the capabilities of DBI tools in combating advanced malware threats.

Deep learning and clustering approaches for dental implant size classification based on periapical radiographs.

This study investigated two artificial intelligence (AI) methods for automatically classifying dental implant diameter and length based on periapical radiographs. The first method, deep learning (DL), involved utilizing the pre-trained VGG16 model and adjusting the fine-tuning degree to analyze image data obtained from periapical radiographs. The second method, clustering analysis, was accomplished by analyzing the implant-specific feature vector derived from three key points coordinates of the dental implant using the k-means++ algorithm and adjusting the weight of the feature vector. DL and clustering model classified dental implant size into nine groups. The performance metrics of AI models were accuracy, sensitivity, specificity, F1-score, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC-ROC). The final DL model yielded performances above 0.994, 0.950, 0.994, 0.974, 0.952, 0.994, and 0.975, respectively, and the final clustering model yielded performances above 0.983, 0.900, 0.988, 0.923, 0.909, 0.988, and 0.947, respectively. When comparing the AI model before tuning and the final AI model, statistically significant performance improvements were observed in six out of nine groups for DL models and four out of nine groups for clustering models based on AUC-ROC. Two AI models showed reliable classification performances. For clinical applications, AI models require validation on various multicenter data.

Fluidic self-assembly for MicroLED displays by controlled viscosity.

Displays in which arrays of microscopic 'particles', or chiplets, of inorganic light-emitting diodes (LEDs) constitute the pixels, termed MicroLED displays, have received considerable attention1,2 because they can potentially outperform commercially available displays based on organic LEDs3,4 in terms of power consumption, colour saturation, brightness and stability and without image burn-in issues1,2,5-7. To manufacture these displays, LED chiplets must be epitaxially grown on separate wafers for maximum device performance and then transferred onto the display substrate. Given that the number of LEDs needed for transfer is tremendous-for example, more than 24 million chiplets smaller than 100 µm are required for a 50-inch, ultra-high-definition display-a technique capable of assembling tens of millions of individual LEDs at low cost and high throughput is needed to commercialize MicroLED displays. Here we demonstrate a MicroLED lighting panel consisting of more than 19,000 disk-shaped GaN chiplets, 45 µm in diameter and 5 µm in thickness, assembled in 60 s by a simple agitation-based, surface-tension-driven fluidic self-assembly (FSA) technique with a yield of 99.88%. The creation of this level of large-scale, high-yield FSA of sub-100-µm chiplets was considered a significant challenge because of the low inertia of the chiplets. Our key finding in overcoming this difficulty is that the addition of a small amount of poloxamer to the assembly solution increases its viscosity which, in turn, increases liquid-to-chiplet momentum transfer. Our results represent significant progress towards the ultimate goal of low-cost, high-throughput manufacture of full-colour MicroLED displays by FSA.

Validation of resonance Raman spectroscopy-measured skin carotenoid status as a biomarker for fruit and vegetable intake in Korean adults.

Blood carotenoid concentration measurement is considered the gold standard for fruit and vegetable (F&V) intake estimation; however, this method is invasive and expensive. Recently, skin carotenoid status (SCS) measured by optical sensors has been evaluated as a promising parameter for F&V intake estimation. In this cross-sectional study, we aimed to validate the utility of resonance Raman spectroscopy (RRS)-assessed SCS as a biomarker of F&V intake in Korean adults. We used data from 108 participants aged 20-69 years who completed SCS measurements, blood collection and 3-d dietary recordings. Serum carotenoid concentrations were quantified using HPLC, and dietary carotenoid and F&V intakes were estimated via 3-d dietary records using a carotenoid database for common Korean foods. The correlations of the SCS with serum carotenoid concentrations, dietary carotenoid intake and F&V intake were examined to assess SCS validity. SCS was positively correlated with total serum carotenoid concentration (r = 0·52, 95 % CI = 0·36, 0·64, P < 0·001), serum ß-carotene concentration (r = 0·60, 95 % CI = 0·47, 0·71, P < 0·001), total carotenoid intake (r = 0·20, 95 % CI = 0·01, 0·37, P = 0·04), ß-carotene intake (r = 0·30, 95 % CI = 0·11, 0·46, P = 0·002) and F&V intake (r = 0·40, 95 % CI = 0·23, 0·55, P < 0·001). These results suggest that SCS can be a valid biomarker of F&V intake in Korean adults.

Concurrent self-assembly of RGB microLEDs for next-generation displays.

MicroLED displays have been in the spotlight as the next-generation displays owing to their various advantages, including long lifetime and high brightness compared with organic light-emitting diode (OLED) displays. As a result, microLED technology1,2 is being commercialized for large-screen displays such as digital signage and active R&D programmes are being carried out for other applications, such as augmented reality3, flexible displays4 and biological imaging5. However, substantial obstacles in transfer technology, namely, high throughput, high yield and production scalability up to Generation 10+ (2,940 × 3,370 mm2) glass sizes, need to be overcome so that microLEDs can enter mainstream product markets and compete with liquid-crystal displays and OLED displays. Here we present a new transfer method based on fluidic self-assembly (FSA) technology, named magnetic-force-assisted dielectrophoretic self-assembly technology (MDSAT), which combines magnetic and dielectrophoresis (DEP) forces to achieve a simultaneous red, green and blue (RGB) LED transfer yield of 99.99% within 15 min. By embedding nickel, a ferromagnetic material, in the microLEDs, their movements were controlled by using magnets, and by applying localized DEP force centred around the receptor holes, these microLEDs were effectively captured and assembled in the receptor site. Furthermore, concurrent assembly of RGB LEDs were demonstrated through shape matching between microLEDs and receptors. Finally, a light-emitting panel was fabricated, showing damage-free transfer characteristics and uniform RGB electroluminescence emission, demonstrating our MDSAT method to be an excellent transfer technology candidate for high-volume production of mainstream commercial products.

Task-Specific Adaptive Differential Privacy Method for Structured Data.

Data are needed to train machine learning (ML) algorithms, and in many cases often include private datasets that contain sensitive information. To preserve the privacy of data used while training ML algorithms, computer scientists have widely deployed anonymization techniques. These anonymization techniques have been widely used but are not foolproof. Many studies showed that ML models using anonymization techniques are vulnerable to various privacy attacks willing to expose sensitive information. As a privacy-preserving machine learning (PPML) technique that protects private data with sensitive information in ML, we propose a new task-specific adaptive differential privacy (DP) technique for structured data. The main idea of the proposed DP method is to adaptively calibrate the amount and distribution of random noise applied to each attribute according to the feature importance for the specific tasks of ML models and different types of data. From experimental results under various datasets, tasks of ML models, different DP mechanisms, and so on, we evaluate the effectiveness of the proposed task-specific adaptive DP method. Thus, we show that the proposed task-specific adaptive DP technique satisfies the model-agnostic property to be applied to a wide range of ML tasks and various types of data while resolving the privacy-utility trade-off problem.

Outcomes of Endoscopic Resection for Early Gastric Cancer in Very Elderly Patients: A Nationwide Population-Based Study.

Background/Aims: Few studies have investigated the long-term outcomes of endoscopic resection for early gastric cancer (EGC) in very elderly patients. The aim of this study was to determine the appropriate treatment strategy and identify the risk factors for mortality in these patients. Methods: Patients with EGC who underwent endoscopic resection from 2006 to 2017 were identified using National Health Insurance Data and divided into three age groups: very elderly (≥85 years), elderly (65 to 84 years), and non-elderly (≤64 years). Their long- and short-term outcomes were compared in the three age groups, and the survival in the groups was compared with that in the control group, matched by age and sex. We also evaluated the risk factors for long- and short-term outcomes. Results: A total of 8,426 patients were included in our study: 118 very elderly, 4,583 elderly, and 3,725 non-elderly. The overall survival and cancer-specific survival rates were significantly lower in the very elderly group than in the elderly and the non-elderly groups. Congestive heart failure was negatively associated with cancer-specific survival. A significantly decreased risk for mortality was observed in all groups (p<0.001). The very elderly group had significantly higher readmission and mortality rates within 3 months of endoscopic resection than the non-elderly and elderly groups. Furthermore, the cerebrovascular disease was associated with mortality within 3 months after endoscopic resection. Conclusions: Endoscopic resection for EGC can be helpful for very elderly patients, and it may play a role in achieving overall survival comparable to that of the control group.

Associations of Cardiorespiratory Fitness With Estimated Remnant Cholesterol and Non-High-Density Lipoprotein Cholesterol in Healthy Men.

Remnant cholesterol (RC) and non-high-density lipoprotein cholesterol (non-HDL-C) may contribute to the residual risk for atherosclerotic cardiovascular disease. High cardiorespiratory fitness (CRF) is associated with favorable traditional lipid profiles, but its relation with RC and non-HDL-C remains unclear. We analyzed cross-sectional data on 4,613 healthy men (mean age 49 years). CRF was measured using peak oxygen uptake during incremental exercise testing and categorized into quartiles. RC was estimated as total cholesterol minus HDL-C and low-density lipoprotein cholesterol, and elevated RC was defined as ≥38 mg/100 ml (90 percentile). Non-HDL-C was calculated as total cholesterol minus HDL-C, and high non-HLD-C was defined as ≥190 mg/100 ml. CRF was inversely associated with RC (ß -0.31, 95% confidence interval [CI] -0.39 to -0.24) and non-HDL-C (ß -0.34, 95% CI -0.57 to -0.11) after adjustment for several risk factors. Each metabolic equivalent increment in CRF was associated with lower odds of having elevated RC (odds ratio [OR] 0.85, 95% CI 0.77 to 0.93) and non-HDL-C (OR 0.93, 95% CI 0.85 to 1.00) in multivariable analysis. Compared with the bottom quartile, the top quartile of CRF had significantly lower odds of elevated RC (OR 0.63, 95% CI 0.45 to 0.88) and non-HDL-C (OR 0.68, 95% CI 0.51 to 0.91). In conclusion, higher CRF was independently associated with lower levels of RC and non-HDL-C and lower odds of the prevalence of elevated RC and non-HDL-C in healthy men.

Clustering Approach for Detecting Multiple Types of Adversarial Examples.

With intentional feature perturbations to a deep learning model, the adversary generates an adversarial example to deceive the deep learning model. As an adversarial example has recently been considered in the most severe problem of deep learning technology, its defense methods have been actively studied. Such effective defense methods against adversarial examples are categorized into one of the three architectures: (1) model retraining architecture; (2) input transformation architecture; and (3) adversarial example detection architecture. Especially, defense methods using adversarial example detection architecture have been actively studied. This is because defense methods using adversarial example detection architecture do not make wrong decisions for the legitimate input data while others do. In this paper, we note that current defense methods using adversarial example detection architecture can classify the input data into only either a legitimate one or an adversarial one. That is, the current defense methods using adversarial example detection architecture can only detect the adversarial examples and cannot classify the input data into multiple classes of data, i.e., legitimate input data and various types of adversarial examples. To classify the input data into multiple classes of data while increasing the accuracy of the clustering model, we propose an advanced defense method using adversarial example detection architecture, which extracts the key features from the input data and feeds the extracted features into a clustering model. From the experimental results under various application datasets, we show that the proposed method can detect the adversarial examples while classifying the types of adversarial examples. We also show that the accuracy of the proposed method outperforms the accuracy of recent defense methods using adversarial example detection architecture.

Joint Associations of Obesity and Cardiorespiratory Fitness With Coronary Artery Calcium Composition: IS THERE EVIDENCE FOR FAT-BUT-FIT?

INTRODUCTION: The purpose of this study was to examine the individual and joint associations of obesity and cardiorespiratory fitness (CRF) with indices of coronary artery calcification (CAC) in 2090 middle-aged men. METHODS: Obesity was defined as a body mass index (BMI) ≥25 kg/m2 and a waist circumference (WC) ≥90 cm. Cardiorespiratory fitness was operationally defined as peak oxygen uptake (V˙o2peak) directly measured using gas analysis. Participants were then divided into unfit and fit categories based on age-specific V˙o2peak percentiles. Agatston scores >100 and volume and density scores >75th percentile were defined as indices of CAC, signifying advanced subclinical atherosclerosis. RESULTS: Obese men had increased CAC Agatston, volume, and density scores, while higher CRF was associated with lower Agatston and volume scores after adjusting for potential confounders. In the joint analysis, unfit-obese men had higher CAC Agatston and CAC volume. The fit-obesity category was not associated with CAC Agatston (OR = 0.91: 95% CI, 0.66-1.25, for BMI and OR = 1.21: 95% CI, 0.86-1.70, for WC) and CAC volume (OR = 1.14: 95% CI, 0.85-1.53, for BMI and OR = 1.23: 95% CI, 0.90-1.69, for WC), which were similar to estimates for the fit-normal weight category. CONCLUSIONS: These findings demonstrate that while obesity is positively associated with the prevalence of moderate to severe CAC scores, CRF is inversely associated with the prevalence of moderate to severe CAC scores. Additionally, the combination of being fit and obese was not associated with CAC scores, which could potentially reinforce the fat-but-fit paradigm.

Is Homomorphic Encryption-Based Deep Learning Secure Enough?

As the amount of data collected and analyzed by machine learning technology increases, data that can identify individuals is also being collected in large quantities. In particular, as deep learning technology-which requires a large amount of analysis data-is activated in various service fields, the possibility of exposing sensitive information of users increases, and the user privacy problem is growing more than ever. As a solution to this user's data privacy problem, homomorphic encryption technology, which is an encryption technology that supports arithmetic operations using encrypted data, has been applied to various field including finance and health care in recent years. If so, is it possible to use the deep learning service while preserving the data privacy of users by using the data to which homomorphic encryption is applied? In this paper, we propose three attack methods to infringe user's data privacy by exploiting possible security vulnerabilities in the process of using homomorphic encryption-based deep learning services for the first time. To specify and verify the feasibility of exploiting possible security vulnerabilities, we propose three attacks: (1) an adversarial attack exploiting communication link between client and trusted party; (2) a reconstruction attack using the paired input and output data; and (3) a membership inference attack by malicious insider. In addition, we describe real-world exploit scenarios for financial and medical services. From the experimental evaluation results, we show that the adversarial example and reconstruction attacks are a practical threat to homomorphic encryption-based deep learning models. The adversarial attack decreased average classification accuracy from 0.927 to 0.043, and the reconstruction attack showed average reclassification accuracy of 0.888, respectively.

Prediction Algorithms for Blood Pressure Based on Pulse Wave Velocity Using Health Checkup Data in Healthy Korean Men: Algorithm Development and Validation.

BACKGROUND: Pulse transit time and pulse wave velocity (PWV) are related to blood pressure (BP), and there were continuous attempts to use these to predict BP through wearable devices. However, previous studies were conducted on a small scale and could not confirm the relative importance of each variable in predicting BP. OBJECTIVE: This study aims to predict systolic blood pressure and diastolic blood pressure based on PWV and to evaluate the relative importance of each clinical variable used in BP prediction models. METHODS: This study was conducted on 1362 healthy men older than 18 years who visited the Samsung Medical Center. The systolic blood pressure and diastolic blood pressure were estimated using the multiple linear regression method. Models were divided into two groups based on age: younger than 60 years and 60 years or older; 200 seeds were repeated in consideration of partition bias. Mean of error, absolute error, and root mean square error were used as performance metrics. RESULTS: The model divided into two age groups (younger than 60 years and 60 years and older) performed better than the model without division. The performance difference between the model using only three variables (PWV, BMI, age) and the model using 17 variables was not significant. Our final model using PWV, BMI, and age met the criteria presented by the American Association for the Advancement of Medical Instrumentation. The prediction errors were within the range of about 9 to 12 mmHg that can occur with a gold standard mercury sphygmomanometer. CONCLUSIONS: Dividing age based on the age of 60 years showed better BP prediction performance, and it could show good performance even if only PWV, BMI, and age variables were included. Our final model with the minimal number of variables (PWB, BMI, age) would be efficient and feasible for predicting BP.

The Relationship Between Breast Density Change During Menopause and the Risk of Breast Cancer in Korean Women.

BACKGROUND: The aim of this study was to investigate the relationship between changes in breast density during menopause and breast cancer risk. METHODS: This study was a retrospective, longitudinal cohort study for women over 30 years of age who had undergone breast mammography serially at baseline and postmenopause during regular health checkups at Samsung Medical Center. None of the participants had been diagnosed with breast cancer at baseline. Mammographic breast density was measured using the American College of Radiology Breast Imaging Reporting and Data System. RESULTS: During 18,615 person-years of follow-up (median follow-up 4.8 years; interquartile range 2.8-7.5 years), 45 participants were diagnosed with breast cancer. The prevalence of dense breasts was higher in those who were younger, underweight, had low parity or using contraceptives. The cumulative incidence of breast cancer increased 4 years after menopause in participants, and the consistently extremely dense group had a significantly higher cumulative incidence (CI) of breast cancer compared with other groups [CI of extremely dense vs. others (incidence rate per 100,000 person-years): 375 vs. 203, P < 0.01]. CONCLUSION: Korean women whose breast density was extremely dense before menopause and who maintained this density after menopause were at two-fold greater risk of breast cancer. PREVENTION RELEVANCE: Extremely dense breast density that is maintained persistently from premenopause to postmenopause increases risk of breast cancer two fold in Korean women. Therefore, women having risk factors should receive mammography frequently and if persistently extremely dense breast had been detected, additional modalities of BC screening could be considered.

Enhancement in external quantum efficiency of AlGaInP red µ-LED using chemical solution treatment process.

To investigate the effects of their surface recovery and optical properties, extremely small sized (12 µm × 12 µm mesa area) red AlGaInP micro light emitting diodes ([Formula: see text] LED) were fabricated using a diluted hydrofluoric acid (HF) surface etch treatment. After the chemical treatment, the external quantum efficiencies (EQEs) of [Formula: see text]-LED at low and high injection current regions have been improved by 35.48% and 12.86%, respectively. The different phenomena of EQEs have a complex relationship between the suppression of non-radiative recombination originating from the etching damage of the surface and the improvement of light extraction of the sidewalls. The constant enhancement of EQE at a high injection current it is attributed to the expansion of the active region's sidewall surface area by the selective etching of AlInP layers. The improved EQE at a low injection current is related to the minimization of the surface recombination caused by plasma damage from the surface. High-resolution transmission electron microscopy (HR-TEM) revealed physical defects on the sidewall surface, such as plasma-induced lattice disorder and impurity contamination damage, were eliminated using chemical treatment. This study suggests that chemical surface treatment using diluted HF acid can be an effective method for enhancing the [Formula: see text]-LED performance.

Proton pump inhibitors use and the risk of fatty liver disease: A nationwide cohort study.

BACKGROUND AND AIM: Proton pump inhibitor (PPI)-induced hypochondria can change the composition of the gut microbiota, inducing overgrowth of small bowel bacteria, which has been suggested to promote the development of fatty liver disease through the gut-liver axis. In this study, we aimed to investigate the association between PPI use and the risk of fatty liver disease. METHODS: A retrospective cohort study was conducted using the Korean National Health Insurance Service-National Sample Cohort, a nationwide population-based representative sample, from January 1, 2002, to December 31, 2015. PPI use was identified from treatment claims and considered as a time-varying variable. RESULTS: During 1 463 556 person-years of follow-up, 75 727 patients had at least one PPI prescription, and 3735 patients developed fatty liver disease. The hazard ratio for fatty liver disease comparing PPI users with non-PPI users was 1.68 (95% confidence interval, 1.61-1.75). When adjusted for multiple confounders, including age, sex, body mass index, smoking, alcohol intake, exercise, income level, and comorbidities, the association was still significant (hazard ratio, 1.50; 95% confidence interval, 1.44-1.57). After considering the amounts of PPIs stratified by cumulative defined daily dose, the dose-response effect was observed until 180 days. Subgroup analysis also revealed that PPI use was correlated to an increased risk of fatty liver disease. CONCLUSIONS: This current national wide cohort study suggests that PPI use was associated with an increased risk of fatty liver disease compared with non-use of PPIs. Clinicians should consider fatty liver as a potential risk when prescribing PPI.

Robust Indoor Localization Methods Using Random Forest-Based Filter against MAC Spoofing Attack.

With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environments with natural noise, some studies were conducted to remove it. So far, two representative methods, i.e., the filtering-based method and deep learning-based method, have shown a significant effect in removing natural noise. However, the prediction accuracy of these methods severely decreased under artificial noise caused by adversaries. In this paper, we introduce a new media access control (MAC) spoofing attack scenario injecting artificial noise, where the prediction accuracy of Wi-Fi-based indoor localization system significantly decreases. We also propose a new deep learning-based indoor localization method using random forest(RF)-filter to provide the good prediction accuracy under the new MAC spoofing attack scenario. From the experimental results, we show that the proposed indoor localization method provides much higher prediction accuracy than the previous methods in environments with artificial noise.

Secure and Blockchain-Based Emergency Driven Message Protocol for 5G Enabled Vehicular Edge Computing.

Basic safety message (BSM) are messages that contain core elements of a vehicle such as vehicle's size, position, speed, acceleration and others. BSM are lightweight messages that can be regularly broadcast by the vehicles to enable a variety of applications. On the other hand, event-driven message (EDM) are messages generated at the time of occurrence such as accidents or roads sliding and can contain much more heavy elements including pictures, audio or videos. Security, architecture and communication solutions for BSM use cases have been largely documented on in the literature contrary to EDM due to several concerns such as the variant size of EDM, the appropriate architecture along with latency, privacy and security. In this paper, we propose a secure and blockchain based EDM protocol for 5G enabled vehicular edge computing. To offer scalability and latency for the proposed scenario, we adopt a 5G cellular architecture due to its projected features compared to 4G tong-term evaluation (LTE) for vehicular communications. We consider edge computing to provide local processing of EDM that can improve the response time of public agencies (ambulances or rescue teams) that may intervene to the scene. We make use of lightweight multi-receiver signcryption scheme without pairing that offers low time consuming operations, security, privacy and access control. EDM records need to be kept into a distributed system which can guarantee reliability and auditability of EDM. To achieve this, we construct a private blockchain based on the edge nodes to store EDM records. The performance analysis of the proposed protocol confirms its efficiency.

Software-Defined Network-Based Vehicular Networks: A Position Paper on Their Modeling and Implementation.

There is a strong devotion in the automotive industry to be part of a wider progression towards the Fifth Generation (5G) era. In-vehicle integration costs between cellular and vehicle-to-vehicle networks using Dedicated Short Range Communication could be avoided by adopting Cellular Vehicle-to-Everything (C-V2X) technology with the possibility to re-use the existing mobile network infrastructure. More and more, with the emergence of Software Defined Networks, the flexibility and the programmability of the network have not only impacted the design of new vehicular network architectures but also the implementation of V2X services in future intelligent transportation systems. In this paper, we define the concepts that help evaluate software-defined-based vehicular network systems in the literature based on their modeling and implementation schemes. We first overview the current studies available in the literature on C-V2X technology in support of V2X applications. We then present the different architectures and their underlying system models for LTE-V2X communications. We later describe the key ideas of software-defined networks and their concepts for V2X services. Lastly, we provide a comparative analysis of existing SDN-based vehicular network system grouped according to their modeling and simulation concepts. We provide a discussion and highlight vehicular ad-hoc networks' challenges handled by SDN-based vehicular networks.