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The article presents the structure and model of an Internet of Things network that can be used for remote rapid detection of Alzheimer’s disease. A local server model of the Internet of Things network has been created for personalized medical care on the client side. The model corresponds to the characteristics of the Internet of Things network: interconnection between devices, real-time communication, data processing and analysis, the use of various protocols for data transfer and exchange. When building the model, the Flask framework was used to create an application instance with a trigger condition for sending data from a smartphone to a local server via an HTTP request. The local server receives the HTTP request sent by the smartphone and processes the data. The result of the procedure is transmitted through the MQTT protocol to the MQTT client that has been subscribed to certain topics, i.e., the smartphone. Taking into account the selected structure and configuration of the Internet of Things network device, a complete model of this network was built, which can be applied to various applications. The functions and performance of the model are verified through experiments.

The subject matter of this article is the methods to detect distributed denial-of-service (DDoS) attacks at the Hypertext Transfer Protocol (HTTP) level with the purpose of justifying the requirements for creating software capable of identifying malicious web server clients. The goal of this article is to develop an information technology to evaluate the efficiency of DDoS attack detection methods, which will quantify their operating time, memory consumption, and approximate classification accuracy. In addition, this paper proposes hypotheses and a potential approach to improve existing application-layer DDoS attack detection methods with the intention of increasing their accuracy and identification speed. The tasks of this study are as follows: to analyse modern methods for detecting application-layer DDoS attacks; to investigate their features and short­comings; to develop a software system to assess DDoS attack detection methods; to programmatically implement these methods and experimentally measure their performance indicators, specifically: classification ac­curacy, operating time, and memory usage; to compare the efficiency of the investigated methods; to formulate hypotheses and propose an approach to improve existing methods and/or develop new methods based on the results obtained. The methods employed are abstraction, analysis, systematic approach, and empirical research. In particular, the datasets generated by DDoS utilities were processed using the synthetic minority oversampling technique (SMOTE) to balance them. Furthermore, the studied DDoS attack detection methods were implemented, including fitting the required parameters and training artificial neural network models for evaluation. The following results were obtained. The average classification accuracy, operating time, and random-access memory (RAM) consumption during Internet traffic classification were determined for six DDoS attack detection methods under the same conditions. This study has demonstrated that the development of a novel method to detect DDoS attacks at the HTTP level with enhanced accuracy and classification speed is strongly required. The experimental results demonstrate that the time series-based method exhibited the shortest operating time (1.33 ms for 5000 vectors), whereas the deep neural network-based method exhibited the highest average classification accuracy (ranging from 99.07% to 99.97%) and the lowest memory consumption (39.09 KB for 5000 vectors). Conclusions. In this study, a software system was developed to assess the average accuracy of DDoS attack classification methods and measure the computational resources utilized. The scientific novelty of the obtained results lies in the formulation of two hypotheses and a potential approach to the creation of a novel method for detecting DDoS attacks at the HTTP level, which will have both high classification accuracy and a short operating time to surpass previously studied analogues in these respects. The first hypothesis is based on the additional usage of HTTP request attributes during Internet traffic classification. The second hypothesis is to analyse a graph of user transitions between website pages. The article also superficially describes a potential approach that involves the implementation of the described hypotheses as well as the proposed software architecture of an application-layer DDoS attack detection system for the Kubernetes platform and the Istio framework, which addresses the issue of collecting web request parameter values for websites that use the cryptographically secured HTTPS protocol.

The subject matter of this article is the methods to detect distributed denial -of-service (DDoS) attacks at the Hypertext Transfer Protocol (HTTP) level with the purpose of justifying the requirements for creating software capable of identifying malicious web server clients. The goal of this article is to develop an information technology to evaluate the efficiency of DDoS attack detection methods, which will quantify their operating time, memory consumption, and approximate classification accuracy. In addition, this paper p roposes hypotheses and a potential approach to improve existing application-layer DDoS attack detection methods with the intention of increasing their accuracy and identification speed. The tasks of this study are as follows: to analyse modern methods for detecting application-layer DDoS attacks; to investigate their features and shortcomings; to develop a software system to assess DDoS attack detection methods; to programmatically implement these methods and experimentally measure their performance indicators, specifically: classification accuracy, operating time, and memory usage; to compare the efficiency of the investigated methods; to formulate hypotheses and propose an approach to improve existing methods and/or develop new methods based on the resul ts obtained. The methods employed are abstraction, analysis, systematic approach, and empirical research. In particular, the datasets generated by DDoS utilities were processed using the synthetic minority oversampling technique (SMOTE) to balance them. Furthermore, the studied DDoS attack detection methods were implemented, including fitting the required parameters and training artificial neural network models for evaluation. The following results were obtained. The average classification accuracy, operating time, and random-access memory (RAM) consumption during Internet traffic classification were determined for six DDoS attack detection methods under the same conditions. This study has demonstrated that the development of a novel method to detect DDoS at tacks at the HTTP level with enhanced accuracy and classification speed is strongly required. The experimental results demonstrate that the time series-based method exhibited the shortest operating time (1.33 ms for 5000 vectors), whereas the deep neural network-based method exhibited the highest average classification accuracy (ranging from 99.07% to 99.97%) and the lowest memory consumption (39.09 KB for 5000 vectors). Conclusions. In this study, a software system was developed to assess the average accuracy of DDoS attack classification methods and measure the computational resources utilized. The scientific novelty of the obtained results lies in the formulation of two hypotheses and a potential approach to the creation of a novel method for detecting DDoS attacks at the HTTP level, which will have both high classification accuracy and a short operating time to surpass previously studied analogues in these respects. The first hypothesis is based on the additional usage of HTTP request attributes during Internet traffic classification. The second hypothesis is to analyse a graph of user transitions between website pages. The article also superficially describes a potential approach that involves the implementation of the described hypotheses as well as the proposed software architecture of an applicationlayer DDoS attack detection system for the Kubernetes platform and the Istio framework, which addresses the issue of collecting web request parameter values for websites that use the cryptographically secure d HTTPS protocol. [ABSTRACT FROM AUTHOR]

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Hereditary TTP (hTTP), termed Upshaw–Schulman syndrome, is an ultra-rare disorder caused by a severe deficiency of plasma ADAMTS13 activity that allows circulation of ultra-large von Willebrand factor (UL-VWF) multimers. The greatest risk for hTTP is in their first days after birth, when 35–50% of patients will have severe hemolysis, jaundice, and thrombocytopenia. It is often fatal without effective treatment. In utero, fetal blood flowing from the pulmonary artery through the ductus arteriosus (DA) to the aorta is under low-shear-force. At birth, blood flow through the DA reverses to a left-to-right shunt, and the diameter of the DA begins to decrease due to hyper-oxygenated blood and decreased plasma prostaglandin E2. This causes turbulent circulation that unfolds UL-VWF, allowing platelet aggregation. If the DA closes promptly, hTTP newborns survive, but if it remains patent, turbulent circulation persists, triggering microvascular thrombosis. hTTP is commonly diagnosed as hemolytic disease of the fetus and newborn (HDFN) caused by anti-red cell antibodies and treated with exchange blood transfusion, which prevents kernicterus even when the diagnosis of hTTP is missed. The diagnosis of newborn-onset hTTP should be considered because HDFN does not cause severe thrombocytopenia, which might be effectively treated with recombinant ADAMTS13. [ABSTRACT FROM AUTHOR]

Hydroponics is based on nutrients in water. It must be regularly monitored to prevent plant defects. The Internet of Things has become a solution for remote hydroponic monitoring and is currently being tested on the Yuan Hidroponik Kelompok Wanita Tani (KWT). This system will send data every minute, and each data has a possibility of loss in transmission. There is a chance that this system will be implemented in other hydroponic organizations. As more devices are involved, it will affect server resources. This research will compare Message Queue Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP) as popular protocols used in IoT. A test with increasing clients shows that at 50 clients HTTP needs 87% CPU, while MQTT needs 22.63% CPU. A test with increasing payload shows that at 10,000 payload HTTP needs 94% CPU while MQTT needs 28.35% CPU. A test with fixed clients and payloads shows that HTTP has a CPU limit based on the clients involved. A transfer time test shows that HTTP needs 177.344 seconds while MQTT needs 3.24 seconds. An acceptance rate is calculated by incrementing the count for every incoming payload. It shows that HTTP can receive 30,000 payloads, unlike MQTT which can only receive 1680 payloads before losses.

The rapid growth of network services and applications has led to an exponential increase in data flows on the internet. Given the dynamic nature of data traffic in the realm of internet content distribution, traditional TCP/IP network systems often struggle to guarantee reliable network resource utilization and management. The recent advancement of the Quick UDP Internet Connect (QUIC) protocol equips media transfer applications with essential features, including structured flowcontrolled streams, quick connection establishment, and seamless network path migration. These features are vital for ensuring the efficiency and reliability of network performance and resource utilization, especially when network hosts transmit data flows over end-to-end paths between two endpoints. QUIC greatly improves media transfer performance by reducing both connection setup time and transmission latency. However, it is still constrained by the limitations of single-path bandwidth capacity and its variability. To address this inherent limitation, recent research has delved into the concept of multipath QUIC, which utilizes multiple network paths to transmit data flows concurrently. The benefits of multipath QUIC are twofold: it boosts the overall bandwidth capacity and mitigates flow congestion issues that might plague individual paths. However, many previous studies have depended on basic scheduling policies, like round-robin or shortest-time-first, to distribute data transmission across multiple paths. These policies often overlook the subtle characteristics of network paths, leading to increased link congestion and transmission costs. In this paper, we introduce a novel multipath QUIC strategy aimed at minimizing flow completion time while taking into account both path delay and packet loss rate. Experimental results demonstrate the superiority of our proposed method compared to standard QUIC, Lowest-RTT-First (LRF) QUIC, and Pluginized QUIC schemes. The relative performance underscores the efficacy of our design in achieving efficient and reliable data transfer in real-world scenarios using the Mininet simulator. [ABSTRACT FROM AUTHOR]

Background: Electronic health records (EHR) emerged as a digital record of the data that is generated in the healthcare. Objectives: In this paper the transfer times of EHRs using the Hypertext Transfer Protocol and WebSocket in both local network and wide area network (WAN) are compared. Methods: A python web application to serve Fast Health Interoperability Resources (FHIR) records is created and the transfer times of the EHRs over both HTTP and WebSocket connection are measured. 45000 test Patient resources in 20, 50, 100 and 200 resources per Bundle transfers are used. Results: WebSocket showed much better transfer times of large amount of data. These were 18 s shorter in the local network and 342 s shorter in WAN for the 20 resource per Bundle transfer. Conclusion: RESTful APIs are a convenient way to implement EHR servers; on the other hand, HTTP becomes a bottleneck when transferring large amount of data. WebSocket shows better transfer times and thus its superiority in such situations. The problem can be addressed by developing a new communication protocol or by using network tunneling to handle large data transfer of EHRs. [ABSTRACT FROM AUTHOR]

This article offers a comprehensive overview of recent literature on the HTTP/2 protocol and conducts an analysis of the security threats and DDoS attack typologies associated with HTTP/2. The investigation revealed that the introduction of new features in HTTP/2 has significantly improved the network transmission speed and utilization. However, these advancements have also brought forth a series of emerging network security risks. This study examines the current state of the art in DDoS attacks tailored for HTTP/2 and their detection methods, proposing future research directions in the field of attack detection. By analyzing the distinctive features of HTTP/2 protocol, the study suggests extending DDoS attack detection techniques established for HTTP/1 to the realm of HTTP/2. Furthermore, the research underscores the ease with which adversaries can exploit the intrinsic multiplexing in HTTP/2 to launch a large number of malicious requests, leading to severe depletion of network bandwidth and exhaustion of valuable server resources. Additionally, it highlights the potential applicability of deep learning algorithms in the context of the HTTP/2 protocol. Additionally, the article proposes strategies to address challenges associated with DDoS attacks and the scarcity of adequate datasets for HTTP/2. This research contributes to a comprehensive understanding of the security implications surrounding the HTTP/2 protocol and provides valuable insights for advancing DDoS attack detection technologies.

The CCCH zinc finger proteins play critical roles in a wide variety of growth, development, and stress responses. Currently, limited reports are available about the roles of animal CCCH proteins in plants. In this study, we report the identification of human TTP (hTTP) with functional similarity to PdC3H17 in a hybrid poplar. hTTP and PdC3H17 shared highly similar tandem CCCH zinc-finger RNA-binding domains. The fragments excluding the CCCH domain of both hTTP and PdC3H17 possessed transcriptional activation activities in yeast cells. Compared to the controls, ectopic expression of hTTP in poplar caused dwarfism, and resulted in significant increases in stem xylem vessel number and photosynthetic and ROS-scavenging abilities, thereby enhancing plant tolerance to drought stress. Our results suggest that hTTP may perform a function in poplar through the PdC3H17-mediated system, and provide an example for the application of animal genes in plants.

Muchas aplicaciones hoy en día utilizan el protocolo de la capa de aplicación denominado HTTP. HTTP/3 es la última versión oficial, fue estandarizado en agosto de 2020, desarrollado como una evolución de HTTP/2 y basado en el protocolo de transporte QUIC. HTTP/3 fue diseñado para mejorar el rendimiento y la eficiencia de las comunicaciones web, especialmente en entornos de red con alta pérdida de paquetes y fluctuaciones de conexión. QUIC es un protocolo de nivel de usuario que se ejecuta sobre UDP para resolver algunas limitaciones y desventajas de TCP en el contexto de las comunicaciones web. Al utilizar UDP, QUIC ofrece ventajas como una menor latencia, mayor eficiencia en redes con alta pérdida de paquetes y la capacidad de establecer conexiones más rápidas. El propósito de este documento es proporcionar una visión general de la propuesta del protocolo QUIC realizada por Google, y un análisis de las evaluaciones realizadas de desempeño de HTTP/3 utilizando QUIC. Por último, se describirá el diseño del protocolo y los mecanismos utilizados: control de flujo, control de congestión, seguridad y multiplexación.

Denial of Service (DoS) attacks have evolved from volumetric attacks to target specific applications and can cripple different services with very limited effort. Hypertext Transfer Protocol (HTTP) is vulnerable to a slow rate DoS attack generated through prolonged connections which deliberately send incomplete requests to server. Simple detection methods which use x number of such connections in y time can be easily evaded. In this paper, we present SlowTrack which can detect slow rate DoS attacks against HTTP using a set of behavioral parameters. SlowTrack uses eight behavioral parameters which are validated to be useful in identifying the attack. We correlate these parameters to understand how their values change when attack is launched and subsequently use these observations to propose detection methods. SlowTrack is composed of three detection algorithms which make use of these observations for detecting attacks. We evaluate the detection performance of SlowTarck using experiments done in a testbed and also in a live network to show that these algorithms can detect the slow rate attacks effectively. [ABSTRACT FROM AUTHOR]

This research paper aims to design an energy-efficient surveillance camera utilizing the ESP32-CAM module, incorporating Wi-Fi connectivity, and using 3D printed custom case made from ABS plastic. The central goal is to determine the most effective video streaming protocol for transmitting real-time data to a web-based interface that manages the camera. The camera prototype is developed through a technical feasibility study and prototype construction methods, with a primary focus on assessing power consumption across various protocols including HTTP, RTP, RTSP, and WebSocket. The findings indicate that WebSocket emerges as one of the most optimal and power-efficient protocols for streaming video from the ESP32-CAM web server to a client device. This superiority arises from WebSocket's browser-based approach, enabling full-duplex communication through a single TCP connection, thereby facilitating real-time interaction between the client and server. [ABSTRACT FROM AUTHOR]

In the burgeoning domain of electric vehicle (EV) technology, advancing supportive ecosystems is pivotal. There is a marked global uptrend in the adoption of EVs, necessitating a robust network of EV charging stations. The infrastructure and accompanying systems that govern these stations are integral to these stations. Increased utilization escalates the exigency for expeditious service at these charging points. This study undertakes a comparative analysis of three distinct data communication protocols at the application layer, specifically within the context of EV charging stations. The protocols scrutinized include Hypertext Transfer Protocol (HTTP), Message Queuing Telemetry Transport (MQTT), and Websocket. The benchmark for data transmission in this investigation is the delivery of energy information, adhering to the Open Charge Point Protocol (OCPP), a legally standardized open protocol. The data format employed is JavaScript Object Notation (JSON). Data transmission utilizing the three protocols above was intercepted and analyzed using Wireshark, a network protocol analyzer. Parameters such as latency (delay), jitter (variability of latency), and throughput (successful data delivery over a communication channel) were meticulously examined and subsequently represented graphically to enhance the interpretability of the network protocol performance. Despite identical data payloads, the findings reveal distinct transmission characteristics for each protocol. HTTP exhibited superior throughput, peaking at 31,621 bits per second (bps) during real-time data transmission. Conversely, MQTT demonstrated the most favorable latency and jitter metrics, both for real-time and periodic data dispatches. Websocket, however, registered the lowest throughput in real-time transmission, at 4,941 bps. These divergences underscore the importance of protocol selection based on specific performance criteria within EV charging station ecosystems. [ABSTRACT FROM AUTHOR]

In the modern era, with the emergence of the Internet of Things (IoT), big data applications, cloud computing, and the ever-increasing demand for high-speed internet with the aid of upgraded telecom network resources, users now require virtualization of the network for smart handling of modern-day challenges to obtain better services (in terms of security, reliability, scalability, etc.). These requirements can be fulfilled by using software-defined networking (SDN). This research article emphasizes one of the major aspects of the practical implementation of SDN to enhance the QoS of a virtual network through the load management of network servers. In an SDN-based network, several servers are available to fulfill users' hypertext transfer protocol (HTTP) requests to ensure dynamic routing under the influence of the SDN controller. However, if the number of requests is directed to a specific server, the controller is bound to follow the user-programmed instructions, and the load on that server is increased, which results in (a) an increase in end-to-end user delay, (b) a decrease in the data transfer rate, and (c) a decrease in the available bandwidth of the targeted server. All of the above-mentioned factors will result in the degradation of network QoS. With the implementation of the proposed algorithm, dynamic active sensing server load management (DASLM), on the SDN controller, the load on the server is shared based on QoS control parameters (throughput, response time, round trip time, etc.). The overall delay is reduced, and the bandwidth utilization along with throughput is also increased. [ABSTRACT FROM AUTHOR]

In the present era, the extensive and diverse utilization of information technology necessitates a swift and efficient response from computer networks. Consequently, network designers have been compelled to enhance the quality and the effectiveness of the logical central control plane. This is done to prevent router misconfigurations and achieve enhanced flexibility in traffic flows, thereby making network management more challenging to attain superior performance. This research paper presents a comprehensive analytical study that compares the most commonly used routing protocols, namely RIP, OSPF, IGRP, and EIGRP. The aim is to determine the most suitable protocol for the network's specific applications. The comparisons have been conducted across various applications, including databases, emails, and HTTP page response time (both sending and receiving). These applications are widely utilized across all types of networks. Numerous experiments and successful scenarios have been carried out by using a simulation program for a proposed computer network. The simulation results underscore the significance of employing the appropriate routing protocol for each specific application. This, in turn, enhances the network's quality of service and overall performance, particularly at the central control level. [ABSTRACT FROM AUTHOR]

Reducing end-to-end latency is a key requirement for efficient and reliable new services offered by next-generation mobile networks. In this context, it is critical for mobile network operators (MNOs) to enable faster communications over backhaul transport networks between next-generation base stations and core networks. However, MNOs will need to make new investments and optimize many points of their current transport infrastructure to serve next-generation services well. In addition, even if MNOs make these investments, there may always be faults and performance degradation in transport networks. This paper presents a new approach to reduce the dependence of MNOs services on the quality of transport networks and rely on software updates on radio access network and core network components. A hyper text transfer protocol (HTTP)-based user plane that can be cached and accelerated is proposed, making it an ideal solution to combat transport problems in next-generation mobile networks. Numerical tests validate our proposed approach and underscore the significant improvements in transfer time, throughput, and overall performance achieved by leveraging HTTP caching and acceleration techniques. More specifically, GPRS tunneling protocol-user plane (GTP-U) is, on average, 35% slower than HTTP, with the performance difference increasing as the data size grows, primarily due to additional overhead and GTP-U encapsulation time. Additionally, HTTP caching with a size of 20 MB provides a 9.5% acceleration in data transfer time, with an average increase of approximately 9% when the data size exceeds 20 MB. [ABSTRACT FROM AUTHOR]

Denial-of-service (DoS) attacks conducted on online systems cause the targeted resources to become inoperative. This is caused by the abnormal traffic intentionally directed toward the system resources by the attacker(s). Several techniques can be used to detect and mitigate such attacks, and machine learning (ML) is one of them. Herein, the literature on ML techniques used to address DDoS attacks is systematically reviewed. Five search engines are used to identify relevant studies, the results are filtered based on specific selection criteria, and 48 studies are shortlisted and analyzed. Considerable variations are observed in the literature with respect to the datasets used for training ML models, with more than 20 datasets being used. To conduct performance evaluations, most studies have employed the accuracy metric. More than 30 modeling algorithms have been used to build the ML models. The outcomes of this review reveal the state of the literature and the manner in which the problem of DDoS attacks has been dealt with. Our results can guide future studies toward more constructive methods than the existing ones to address this problem. [ABSTRACT FROM AUTHOR]

In the course of efforts to develop and define uniform routines for the implementation of Internet of Things (IoT) in industrial environments, it has become essential to integrate industrial communication standards such as OPC UA in the context of IoT ontologies and implications for practical implementation. The Sensor, Observation, Sampling, and Actuator Ontology (SOSA) offers the possibility of mapping OPC UA services such as read and write functions to the underlying system in the sense of IoT. As an aid for the practical implementation of industrial use cases, an architecture based on the considerations of a generic modular system is proposed. Variable elements in the implementation of OPC UA interfaces are identified and discussed. The architectural approach is being evaluated by implementing an OPC UA server and supplementary applications in order to embed machine tools with different control systems in industrial production networks. [ABSTRACT FROM AUTHOR]

We have observed a boom in video streaming over the Internet, especially during the Covid-19 pandemic, that could exceed the network resource availability. In addition to upgrading the network infrastructure, finding a way to smartly adapt the streaming system to the network and users’ conditions to satisfy clients’ perceptions is exceptionally critical, too. This paper proposes a new QoE-aware adaptive streaming scheme over HTTP - ABRA - to make flexible adaptations based on the network and the client’s current status. Besides, we propose a technique that can keep the buffer at an average high for more than 10s. We were limiting the phenomena of rebuffering due to unexpected and unpredictable bandwidth changes. The algorithm keeps the quality of subsequent versions’ quality constant even when the average bitrate decreases, increasing the QoE. Experimental results show that our method can improve QoE from 7.86% to 20.41% compared to state-of-the-art methods. ABRA can provide better performance in terms of QoE score in all buffer conditions compared to the existing solutions while maintaining a minimum secured buffer level for the worst case.