Application Layer Research Articles

Metaverse-oriented visual art quality enhancement strategies: a field architecture design and fuzzy assessment theory perspective

Visual art was originally measured by viewing and appreciating graphic works, and there was no previous research into ways to improve the quality of visual art. With the rapid development of visual arts and technology, the question of how to improve quality has become an urgent one. As the most cutting-edge and hottest concept in the international arena today, the development and application of metaverse technology has widely drawn the close attention of various industries, including management, economy, education, and art. However, there is no in-depth and clear research on the concept of metaverse in the field of art, especially in the field of visual art. We believe that the creation of visual art in the context of metaverse will be an important direction for art development in the future, and can also greatly contribute to the improvement of the quality of metaverse visual art presentation. Therefore, we focus on the issue of visual art quality assessment in our research, and propose a theory and method of metaverse-oriented future visual art quality assessment. The method focuses on the G1-entropy value method to calculate the weights in visual arts, combines qualitative research with quantitative research, and proposes the improvement path and countermeasures for visual arts. In summary, our research aims to address the theoretical approaches to the design of the metaverse field architecture and the assessment of art quality for the future introduction of the metaverse. The main contributions of our research are focused on the following three aspects: 1. The construction of the visual art field architecture draws on the functional requirements analysis method of system science simulation, considering that the entire visual art metaverse field architecture is constructed at three levels: the bottom data support layer, the middle technical support layer and the upper technical application layer. 2. The G1-entropy combination weighting method is used to derive the importance ranking of visual art quality indicators and identify key factors, and to derive suggestions for quality improvement based on the key indicator factors. More importantly, we also build a field architecture for future-oriented visual arts in this study, which bridges the gap in the structural design of visual arts after the introduction of the future concept. Our present study makes a great contribution to the application of visual art quality enhancement, focusing on the analysis of new concepts and the improvement of old methods, building a new scene of organic combination of new technologies and traditional visual art, with practical research theoretical support for the promotion and progress of the disciplinary field.

The Composite TiO2-CuOx Layers Formed by Electrophoretic Method for CO2 Gas Photoreduction.

This study demonstrates the ability to control the properties of TiO2-CuOx composite layers for photocatalytic applications by using a simple electrophoretic deposition method from isopropanol-based suspension. To obtain uniform layers with a controlled composition, the surfactant sodium lauryl sulfate was used, which influenced the electrophoretic mobility of the particles and the morphology of the deposited layers. The TiO2-CuOx composite layers with different CuOx contents (1.5, 5.5, and 11 wt.%) were obtained. It is shown that the optical band gap measured by UV-VIS-NIR diffuse reflectance spectra. When CuOx is added to TiO2, two absorption edges corresponding to TiO2 and CuOx are observed, indicating a broadening of the photosensitivity range of the material relative to pure TiO2. An open-circuit potential study shows that by changing the amount of CuOx in the composite material, one can control the ratio of free charge carriers (n and p) and, therefore, the catalytic properties of the material. As a result, the TiO2-CuOx composite layers have enhanced photocatalytic activity compared to the pure TiO2 layer: methanol yield grows with increasing CuOx content during CO2 photoreduction.

Research on the Integration of Cloud Computing and Education Resource Sharing Scheme

Through the study of cloud computing technology, we analyze the significance of the integration of cloud computing and education resource sharing, and design a four-layer model architecture for the integration of cloud computing and education resource sharing platform based on the three-layer architecture of cloud computing, and elaborate the specific implementation functions of its resource layer, platform layer, application layer and login access layer, respectively, to deeply integrate cloud computing and education resource sharing.

Tigger: A Database Proxy That Bounces with User-Bypass

Developers often deploy database-specific network proxies whereby applications connect transparently to the proxy instead of directly connecting to the database management system (DBMS). This indirection improves system performance through connection pooling, load balancing, and other DBMS-specific optimizations. Instead of simply forwarding packets, these proxies implement DBMS protocol logic (i.e., at the application layer) to achieve this behavior. Consequently, existing proxies are user-space applications that process requests as they arrive on network sockets and forward them to the appropriate destinations. This approach incurs inefficiencies as the kernel repeatedly copies buffers between user-space and kernel-space, and the associated system calls add CPU overhead. This paper presents user-bypass, a technique to eliminate these overheads by leveraging modern operating system features that support custom code execution. User-bypass pushes application logic into kernel-space via Linux's eBPF infrastructure. To demonstrate its benefits, we implemented Tigger, a PostgreSQL-compatible DBMS proxy using user-bypass to eliminate the overheads of traditional proxy design. We compare Tigger's performance against other state-of-the-art proxies widely used in real-world deployments. Our experiments show that Tigger outperforms other proxies --- in one scenario achieving both the lowest transaction latencies (up to 29% reduction) and lowest CPU utilization (up to 42% reduction). The results show that user-bypass implementations like Tigger are well-suited to DBMS proxies' unique requirements.

QueryBooster: Improving SQL Performance Using Middleware Services for Human-Centered Query Rewriting

SQL query performance is critical in database applications, and query rewriting is a technique that transforms an original query into an equivalent query with a better performance. In a wide range of database-supported systems, there is a unique problem where both the application and database layer are black boxes, and the developers need to use their knowledge about the data and domain to rewrite queries sent from the application to the database for better performance. Unfortunately, existing solutions do not give the users enough freedom to express their rewriting needs. To address this problem, we propose QueryBooster, a novel middleware-based service architecture for human-centered query rewriting, where users can use its expressive and easy-to-use rule language (called VarSQL) to formulate rewriting rules based on their needs. It also allows users to express rewriting intentions by providing examples of the original query and its rewritten query. QueryBooster automatically generalizes them to rewriting rules and suggests high-quality ones. We conduct a user study to show the benefits of VarSQL to formulate rewriting rules. Our experiments on real and synthetic workloads show the effectiveness of the rule-suggesting framework and the significant advantages of using QueryBooster for human-centered query rewriting to improve the end-to-end query performance.

Enhancing the absorption figure of merit on solution-based CuO thin films by Ni doping

We evaluated an absorbance effectiveness on the solar spectrum using an absorption figure of merit (a-FOM) for the conducting thin films. In particular, Ni-doped and un-doped CuO thin films were deposited by a facile and sustainable solution-processed synthesis, whose Ni doping concentration was varied to be 0.2, 0.6, 1, 2, 3, 4 wt%. We observed a change of smooth surface to an appearance of nanoparticles by Ni doping, which supported to form of a plasmonic mechanism for improving the light capture and retention. The absorption of long wavelengths was improved and extended to the near-infrared range. That is, the bandgap energy decreased from 2.10 to 1.88 eV with Ni doping. Also, we found that the absorption length decreased from 99.93 nm to 63.80 nm as the Ni doping increased. In addition, the CuO-based film with 4 wt% Ni doping showed a maximal value of a-FOM as high as 30.88 Ω−1cm−1, corresponding to a resistance of 2.07 MΩ/sq and an absorption length of 63.80 nm. Our finding suggested that Ni-doped CuO thin films can be considered as an excellent selection of absorbent conductive oxide layers for application in optoelectronic devices and solar cell systems.

A framework of genealogy knowledge reasoning and visualization based on a knowledge graph

PurposeWith the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.Design/methodology/approachBased on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.FindingsThe case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.Originality/valueThis study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

Towards Universal Industrial Augmented Reality: Implementing a Modular IAR System to Support Assembly Processes

While Industrial Augmented Reality (IAR) has many applications across the whole product lifecycle, most IAR applications today are custom-built for specific use-cases in practice. This contribution builds upon a scoping literature review of IAR data representations to present a modern, modular IAR architecture. The individual modules of the presented approach are either responsible for user interface and user interaction or for data processing. They are use-case neutral and independent of each other, while communicating through a strictly separated application layer. To demonstrate the architecture, this contribution presents an assembly process that is supported once with a pick-to-light system and once using in situ projections. Both are implemented on top of the novel architecture, allowing most of the work on the individual models to be reused. This IAR architecture, based on clearly separated modules with defined interfaces, particularly allows small companies with limited personnel resources to adapt IAR for their specific use-cases more easily than developing single-use applications from scratch.

Resilience-by-design in Adaptive Multi-agent Traffic Control Systems

Connected and Autonomous Vehicles (CAVs) with their evolving data gathering capabilities will play a significant role in road safety and efficiency applications supported by Intelligent Transport Systems (ITSs), such as Traffic Signal Control (TSC) for urban traffic congestion management. However, their involvement will expand the space of security vulnerabilities and create larger threat vectors. In this article, we perform the first detailed security analysis and implementation of a new cyber-physical attack category carried out by the network of CAVs against Adaptive Multi-Agent Traffic Signal Control (AMATSC), namely, coordinated Sybil attacks, where vehicles with forged or fake identities try to alter the data collected by the AMATSC algorithms to sabotage their decisions. Consequently, a novel, game-theoretic mitigation approach at the application layer is proposed to minimize the impact of such sophisticated data corruption attacks. The devised minimax game model enables the AMATSC algorithm to generate optimal decisions under a suspected attack, improving its resilience. Extensive experimentation is performed on a traffic dataset provided by the city of Montréal under real-world intersection settings to evaluate the attack impact. Our results improved time loss on attacked intersections by approximately 48.9%. Substantial benefits can be gained from the mitigation, yielding more robust adaptive control of traffic across networked intersections.

Remote Patient Activity Monitoring System by Integrating IoT Sensors and Artificial Intelligence Techniques.

Even with the most cutting-edge tools, treating and monitoring patients-including children, elders, and suspected COVID-19 patients-remains a challenging activity. This study aimed to track multiple COVID-19-related vital indicators using a wearable monitoring device with an Internet of Things (IOT) focus. Additionally, the technology automatically alerts the appropriate medical authorities about any breaches of confinement for potentially contagious patients by tracking patients' real-time GPS data. The wearable sensor is connected to a network edge in the Internet of Things cloud, where data are processed and analyzed to ascertain the state of body function. The proposed system is built with three tiers of functionalities: a cloud layer using an Application Peripheral Interface (API) for mobile devices, a layer of wearable IOT sensors, and a layer of Android web for mobile devices. Each layer performs a certain purpose. Data from the IoT perception layer are initially collected in order to identify the ailments. The following layer is used to store the information in the cloud database for preventative actions, notifications, and quick reactions. The Android mobile application layer notifies and alerts the families of the potentially impacted patients. In order to recognize human activities, this work suggests a novel integrated deep neural network model called CNN-UUGRU which mixes convolutional and updated gated recurrent subunits. The efficiency of this model, which was successfully evaluated on the Kaggle dataset, is significantly higher than that of other cutting-edge deep neural models and it surpassed existing products in local and public datasets, achieving accuracy of 97.7%, precision of 96.8%, and an F-measure of 97.75%.

Optimization of Ternary InxGa1-xN Quantum Wells on GaN Microdisks for Full-Color GaN Micro-LEDs

Red, green, and blue light InxGa1−xN multiple quantum wells have been grown on GaN/γ-LiAlO2 microdisk substrates by plasma-assisted molecular beam epitaxy. We established a mechanism to optimize the self-assembly growth with ball-stick model for InxGa1-xN multiple quantum well microdisks by bottom-up nanotechnology. We showed that three different red, green, and blue lighting micro-LEDs can be made of one single material (InxGa1-xN) solely by tuning the indium content. We also demonstrated that one can fabricate a beautiful InxGa1-xN-QW microdisk by choosing an appropriate buffer layer for optoelectronic applications.

CoMn2O4 and Cu0.5Co0.5Mn2O4 spinels as efficient protective coating layers on SUS430 for SOFC interconnect

In this study, CoMn2O4 and Cu0.5Co0.5Mn2O4 spinels are examined as protective coatings for chromium diffusion in metallic interconnect of solid oxide fuel cell. Both spinels are prepared by co-precipitation technique in order to obtain high temperature conducting spinels. The spinels obtained after 4 h of calcinations in air at 500 °C exhibit tetragonal crystal structure with average grain size between 90 and 100 nm. Cu0.5Co0.5Mn2O4 spinel exhibits superior electrical conductivity over CoMn2O4 and both display NTCR (negative temperature co-efficient of resistance) behavior illustrating their use as a protective layer on interconnects for fuel cells operating at higher temperatures. Doctor-blade employed spinel coating over SUS430 alloy exhibits good adhesion between protective spinel layer and metallic alloy. Cross section micrographs reveal presence of nanosized and highly dense grains capable to resist the outflow of chromium ions present in SUS430. Mass gain and diffusion rate constant (Kp) was high for CoMn2O4 coated SUS430 illustrating Cu0.5Co0.5Mn2O4 spinel as a better protective coating layer for interconnect application. Chromium outflow is restricted and the same is evident from all cross-section micrographs of spinel coated SUS430 specimens subjected for oxidation up to 1000 h. Estimated thickness of Cr2O3 layer was ∼15 nm for CoMn2O4 whereas Cu0.5Co0.5Mn2O4 coated SUS430 specimen had ∼12.5 μm thickness at the SUS430/spinel interface demonstrating superior resistance to chromium outflow. The area specific resistance was lowest for Cu0.5Co0.5Mn2O4 coated specimen (between 14 mΩcm2 – 15 mΩcm2) followed by CoMn2O4 coated specimen reiterating Cu0.5Co0.5Mn2O4 as efficient protective coating material for SUS430 SOFC interconnect.

Achieving model explainability for intrusion detection in VANETs with LIME.

Vehicular ad hoc networks (VANETs) are intelligent transport subsystems; vehicles can communicate through a wireless medium in this system. There are many applications of VANETs such as traffic safety and preventing the accident of vehicles. Many attacks affect VANETs communication such as denial of service (DoS) and distributed denial of service (DDoS). In the past few years the number of DoS (denial of service) attacks are increasing, so network security and protection of the communication systems are challenging topics; intrusion detection systems need to be improved to identify these attacks effectively and efficiently. Many researchers are currently interested in enhancing the security of VANETs. Based on intrusion detection systems (IDS), machine learning (ML) techniques were employed to develop high-security capabilities. A massive dataset containing application layer network traffic is deployed for this purpose. Interpretability technique Local interpretable model-agnostic explanations (LIME) technique for better interpretation model functionality and accuracy. Experimental results demonstrate that utilizing a random forest (RF) classifier achieves 100% accuracy, demonstrating its capability to identify intrusion-based threats in a VANET setting. In addition, LIME is applied to the RF machine learning model to explain and interpret the classification, and the performance of machine learning models is evaluated in terms of accuracy, recall, and F1 score.

Data security in smart devices: Advancement, constraints and future recommendations

AbstractNetwork security protocols are implemented to address network security challenges. Computer networks and applications have advanced and developed significantly in recent years, but consumers ‘excitement for network technology and high‐tech devices has been dampened by continual exposure to data security vulnerabilities. As of now, some individuals refuse to use smart devices due to concerns about the authenticity, confidentiality and integrity of data security leaks. This not only prompts Internet service providers to follow market protection mechanisms but also requires software developers to apply appropriate security protocols to protect computer network security. These applications’ dependability and integrity are dependent not just on the effectiveness of cryptographic algorithms, but also on key management protocols. Understanding network security protocols and implementing high‐quality standards to govern the transmission of data in the network are critical components of guaranteeing network security. The article explores data security, primarily at the application layer, various attack methods for different network security protocols and highlights the potential security implications. The study also looks at the corresponding, practical security measures and future research prospects for certain kinds of attacks. Finally, some technical challenges that remain unsolved at the time of writing are summarised, and future trends in cybersecurity are discussed.

Growth and Exploration of Inorganic Semiconductor Electron and Hole Transport Layers for Low-Cost Perovskite Solar Cells

The perovskite exhibited outstanding performance and was a promising alternative material for a low-cost, high power conversion efficiency (PCE) solar cell application. To avoid the high-cost organic materials as electron transport layers (ETL) and hole transport layers (HTL) in perovskite solar cells (PSCs), here introduce the inorganic semiconductor nanomaterials ZnS and CuS work as an ETL and HTL, respectively. In this work, we selected chalcogenides such as zinc sulfide (ZnS) and copper sulfide (CuS) as the 2-electron and hole transport layers and utilized them for perovskite solar cell application. For the proposed cell structure FTO/ZnS/perovskite (CH3NH3PbI3)/CuS/Ag, the deposition of layers has been achieved via different techniques such as thermal evaporation, spin coating and doctor blade, respectively. X-ray diffraction and Field effect scanning electron microscopy (FESEM) with Energy-dispersive X-ray spectroscopy were used to characterize the structural and morphological properties of the prepared samples. UV-Visible spectrophotometer and current density-voltage curve were used to measure the optical and electrical parameters of the deposited layers, respectively. From the J-V characteristics, for the proposed and fabricated PSCs, the estimated PCE is about 0.28 %, open-circuit voltage (VOC) = 0.29 V, and short-circuit current density (JSC) = 3.96 mA/cm2. The results are good and the inorganic nanomaterial layers used in this study are promising for future studies. HIGHLIGHTS In this study, chalcogenide materials such as zinc sulphide (ZnS) as the electron transport layer and cadmium sulfide (CdS) as the hole transport layer in solar perovskite cell applications were investigated Use easy and simple deposit methods such as chemical bath deposition and doctor blade method The possibility of using chalcogenide materials in the field of perovskite solar cells, although the efficiency of the obtained cell is very small, is an indication of the response of such materials in the application of perovskite solar cells GRAPHICAL ABSTRACT

Improvement of optical and conductivity properties of SnS2 via Cr doping for photovoltaic applications

Tin disulfide doped with chromium (Sn1−xCrxS2) powders with different molar ratios (x = 0, 2, 4, 6, and 10 at%) were synthesized using a low-cost hydrothermal technique. The prepared samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), high-resolution transmission electron microscopy (HRTEM), UV-Vis spectroscopy, and electrochemical impedance spectroscopy (EIS). The Sn1−xCrxS2 samples maintained the hexagonal structure with space group (S.G.) P-3m1 from the SnS2 structure, according to XRD and Raman measurements. Cr doping was confirmed by EDS, where Cr partially substituted Sn in the SnS2 structure. SEM images also displayed good homogeneity and smaller particle sizes as Cr doping increased. HRTEM and selected-area electron diffraction (SAED) analysis showed high crystallinity of the synthesized samples. The optical bandgap energy (Eg) of the Cr-doped samples with x = 6% was found to be the lowest among the measured values, and even lower than that of the pure SnS2 sample. The conductivity of the samples was measured through-plane using EIS, and the Sn1−xCrxS2 samples with x = 6% again showed superior conductivity performance within the investigated temperature ranges. These findings further support the potential of Cr-doped SnS2 systems for use as a buffer layer in photovoltaic applications.

Thermochemical oxidation of commercially pure titanium; controlled formation of robust white titanium oxide layers for biomedical applications.

This study addresses controlled formation of white rutile surface layers on commercially pure (CP) titanium by gaseous thermochemical oxidation. The formed oxide layers were investigated with light optical microscopy (LOM), scanning electron microscopy (SEM), X-ray diffraction (XRD), glow discharge optical emission spectroscopy (GDOES), transmission electron microscopy - energy-dispersive X-ray spectroscopy (TEM-EDXs), spectrophotometry, thermogravimetric analysis (TGA), and Vickers micro-indentation. The oxidation response of CP titanium in different single gas systems, O2, N2O, or CO2, at temperatures ranging from 750 °C to 1000 °C, showed that the formed oxide scales exhibit oxide stratification, irrespective of the applied gas. Additionally, a two-step oxidation process was found to result in controlled growth of robust, dense, adherent white titanium oxide layers. The two-step process entails a first oxidation step in an atmosphere of CO/CO2 at 750 °C; a second oxidation step is performed in N2/N2O at 650 °C. The oxidation in a CO/CO2 atmosphere results in the incorporation of carbon in the forming oxide layer. TEM-EDXs analysis after oxidation in CO/CO2 revealed that carbon resides in thin interlayers between slightly stratified 200-300 nm layers of rutile. This unique “composite” oxide layer containing carbon was found to be robust and densely adhering to the substrate. After the second oxidation step in N2O carbon was “retracted” (oxidized) from the oxide‑carbon composite layer, resulting in an aesthetically pleasing, adherent white oxide layer. Finally, this two-step process, leading to robust white oxide layer formation, is show-cased on a biomedical demonstrator part for dental applications.

Investigation and optimization of an effective fabrication process to develop a high-quality CZTS thin layer for photovoltaic applications

Despite its outstanding advantages, its relatively low practical efficiency has prevented CZTS from replacing CIGS. The most important reason for this drawback is known to be the poor crystallization and high porosity of the produced CZTS thin films, causing defect-related recombination. In this work, an organic-based sol-gel method has been used to develop CZTS thin films and several parameters have been examined to optimize the developed thin film quality. Controlled parameters include the molar ratio of precursor solution, drying atmosphere of the samples, thermal annealing, and sulfurization concurrent to thermal annealing. The effect of each parameter was experimentally evaluated using the produced samples' structural, morphological, optical, and electrical characterization. The experimental details of the synthesis and post-processing procedures are presented in addition to the results of performed characterizations such as XRD, UV–vis, Raman spectroscopy, SEM micrographs, EDS analysis, and sheet resistance, which are discussed in this article. Finally, the optimized thin film's response to different wavelengths was evaluated and exhibited an excellent response to the applied wavelengths. The findings of this study would be useful in the development of high-quality CZTS thin films for photovoltaic applications.

Long-term effects of gypsum on the chemistry of sodic soils

Soils with high exchangeable sodium contents have structural and hydrological problems that limit agricultural productivity. Gypsum is commonly applied as an ameliorant but there are few field studies into its long-term (> 10 years) effects on soil chemistry, especially at depths greater than 0.4 m, and no long-term validation of the gypsum requirement model. The aim of this study was to address these two knowledge gaps. This was achieved by a) carrying out a field study of the effects of repeated gypsum applications, irrigation, and sugarcane cultivation to 1.25 m depth over a 26-year period in Australia, and b) assessing the ability of the gypsum requirement model to predict changes in exchangeable sodium percentage (ESP) more broadly, using 53 unique cases from 17 field studies (> 1 year). In our field study, the greatest reductions in exchangeable sodium (up to 5.49 %; p < 0.05) occurred between 0.25 and 1.00 m depth, where initial concentrations were highest. The amount of sodium (16.47 cmolc/m2), magnesium (2.49 cmolc/m2), and potassium (0.65 cmolc/m2) displaced was less than the amount of calcium gained (36.76 cmolc/m2) over the profile. Most calcium was retained in the application layer (0.00–0.25 m) but some moved down to at least 1.25 m. Reductions in exchangeable sodium were smaller than in all previous long-term studies, possibly due to lower deep drainage rates (39–45 mm/year) and increased pH. Assessment of the gypsum requirement model showed that predicted final ESP deviated widely from measured ESP in many cases (95 % of predictions deviated by >10 %), presumably due to interactions between pH, clay type, carbonate, climate, management, and cation exchange equilibria. By adjusting two model parameters, the effect of gypsum on ESP was more accurately predicted (20 % of predictions were within 10 % of measured ESP) making it a useful tool for farmers.

Application Layer-Based Denial-of-Service Attacks Detection against IoT-CoAP

Internet of Things (IoT) is a massive network based on tiny devices connected internally and to the internet. Each connected device is uniquely identified in this network through a dedicated IP address and can share the information with other devices. In contrast to its alternatives, IoT consumes less power and resources; however, this makes its devices more vulnerable to different types of attacks as they cannot execute heavy security protocols. Moreover, traditionally used heavy protocols for web-based communication, such as the Hyper Text Transport Protocol (HTTP) are quite costly to be executed on IoT devices, and thus specially designed lightweight protocols, such as the Constrained Application Protocol (CoAP) are employed for this purpose. However, while the CoAP remains widely-used, it is also susceptible to attacks, such as the Distributed Denial-of-Service (DDoS) attack, which aims to overwhelm the resources of the target and make them unavailable to legitimate users. While protocols, such as the Datagram Transport Layer Security (DTLS) and Lightweight and the Secure Protocol for Wireless Sensor Network (LSPWSN) can help in securing CoAP against DDoS attacks, they also have their limitations. DTLS is not designed for constrained devices and is considered as a heavy protocol. LSPWSN, on the other hand, operates on the network layer, in contrast to CoAP which operates on the application layer. This paper presents a machine learning model, using the CIDAD dataset (created on 11 July 2022), that can detect the DDoS attacks against CoAP with an accuracy of 98%.