Hypertext Transfer Protocol Research Articles

An IoT-Based Home Automation and Weather Monitoring System

This paper reports an IoT-based home automation and weather monitoring system developed for remote control of household appliances and weather monitoring. The proposed system comprises a Wi-Fi enabled ESP32 microcontroller as the network gateway, a relay module, and a DHT11 sensor for monitoring temperature and humidity. The status of the household devices and the weather data are continuously collected and stored in a MySQL database, which is set up on the IoT platform. A user interface was created using two different protocols – HyperText Transfer Protocol (HTTP) and Message Queuing Telemetry Transport (MQTT). The IoT-based home automation and weather monitoring system is designed to be low cost, user-friendly and can remotely control home appliances and monitor weather data over the internet.

A Comparison of Various IoT Application Layer Protocol

Data communication over the internet is highly swayed by protocols. So, the protocols have a very crucial role in data communication. Similarly, for the Internet of things (IoT) enabled devices the protocols have also an important role. In the Application-layer of IoT-enabled devices, the protocols have a significant role in the transfer of data from server to IoT devices and vice versa. There are different protocols are there, some are taken from web based internet protocols like Hyper Text Transfer Protocol (HTTP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), etc. And some are exclusively developed for IoT devices like Message Queue Telemetry Transport (MQTT), Constraint Application Protocol (CoAP), etc. IoT-enabled devices need a lightweight, secure and fast protocols for the transfer of data. When IoT enabled devices are developed there are limited number of protocols developed for communication of IoT devices. Various protocols are developed to fulfil the need of users but they have some advantages as well as disadvantages. In this paper, analysis of the performance and comparision of these protocols through different scenarios are given.

Session Management for Security Systems in 5G Standalone Network

As 5G telecom services evolve rapidly across a broad technological environment, network security in 5G landscape emerges as a critically challenging issue. One of typical network security tools is an intrusion prevention system (IPS) that monitors a network for malicious activity across the cyber-attack chain and takes action to prevent it. Vulnerabilities in 5G core networks become more varied and protocols become increasingly complex, whereby conventional Next Generation Firewall (NGFW) is not enough anymore to respond to cyber attacks. As a typical 5G vulnerability attack, PFCP-in-GTP and IPSec disable attack are highly complex to detect and cannot identify attackers without integrated session management. However, the 5G core network uses various protocols such as Non-Access Stratum (NAS), Hyper Text Transfer Protocol (HTTP), Packet Forwarding Control Protocol (PFCP), and GPRS Tunnelling Protocol (GTP), and packets of the interface used by each protocol are managed as identities that are difficult to identify. Analyzing the relationship of these interfaces in real time is an important key to integrated session management. In addition, unlike existing 4G, as 3rd Generation Partnership Project (3GPP) specs mandate encrypting 5G Standalone (SA) user IDs, it is much more difficult to identify from which user traffic has occurred in IPSs exclusive for cellular network. With regard to the above subject, this paper introduces an efficient session management scheme for users not affordable in conventional NFGW but necessarily useful for security systems in 5G SA. Furthermore, this study compared performances between conventional NGFWs and a 5G IPS system with the scheme employed, to ascertain that the scheme is feasibly implementable in 5G SA network. The actual test results show a detection rate of 99.7% and reasonable resource overhead (Memory usage 37.8%, CPU usage 42–44%).

Access protocol aware controller design for eMBB traffic in SD-CDN

Under a tremendous increase in 5th Generation (5G) traffic, the conventional Domain Name Server(DNS)-based routing in Content Delivery Network (CDN) cannot meet enhanced Mobile BroadBand (eMBB) communication requirements. It routes the traffic flow to the local CDN server which is closest to the end-user geographically using Border Gateway Protocol (BGP). Therefore; the overloading in the local CDN server is inevitable. According to literature, the access types of eMBB content achieve a higher hit-ratio in CDN caching and the anycast-based routing can redirect some of the traffic flows to the original server despite having a geographical disadvantage. Therefore, we consider access protocol types of a user such as TCP-based Hyper-Text Transfer Protocol 2 (HTTP2) and Quick User Datagram Protocol Internet Connections (QUIC) which are commonly used today in iphone Operating System (ioS) and Android-based applications. However, they do not have a central view to select optimal server according to access type in CDN and they are unaware of network dynamics such as protocol-based load and heterogeneity; i.e. the number of total matches packets and the ratio of the packets according to access protocols (HTTP2/QUIC). Therefore, we propose access protocol aware Software Defined Content Delivery Networks (SD-CDN) for eMBB traffic which decouples Data and Control planes and enables easy implementation of anycast routing on this infrastructure. It dynamically takes statistics from OpenFlow switches in the Data plane to calculate a novel load and the heterogeneity metrics per device periodically. It embeds the OpenFlow rules by using a novel access protocol-aware routing algorithm in SD-CDN. According to performance evaluation in a real test-bed environment; the Quality of Server (QoS) of received eMBB content by end-user from either the local or the original CDN servers, the proposed SD-CDN outputs 60% better PSNR than the conventional DNS-based routing with an acceptable rate for 5G requirements.

Developed security and privacy algorithms for cyber physical system

Cyber-physical system (CPS) is a modern technology in the cyber world, and it integrates with wireless sensor network (WSN). This system is widely used in many applications such as a smart city, greenhouse, healthcare, and power grid. Therefore, the data security and integrity are necessary to ensure the highest level of protection and performance for such systems. In this paper, two sides security system for cyber-physical level is proposed to obtain security, privacy, and integrity. The first side is applied the secure sockets layer (SSL)/transport layer security (TLS) encryption protocol with the internet of things (IoT) based message queuing telemetry transport (MQTT) protocol to secure the connection and encrypt the data exchange between the system's parties. The second side proposes an algorithm to detect and prevent a denial of service (DoS) attack (hypertext transfer protocol (HTTP) post request) on a Web server. The experiment results show the superior performance of the proposed method to secure the CPS by detecting and preventing the cyber-attacks, which infect the Web servers. They also prove the implementation of security, privacy and integrity aspects on the CPS.

E-commerce cybersecurity vulnerability, http protocol case

This paper was designed with the intention of analyzing the vulnerability of e-commerce web sites. We are going to describe the transmission of data in clear text without encryption by hypertext transfer protocol (HTTP), reason why we argue that by means of a sniffer technique this information can be captured and this way violating customer privacy information.

An enhanced deep learning based framework for web attacks detection, mitigation and attacker profiling

Protecting web applications is becoming challenging every passing day, primarily because of attack sophistication, omnipresence of web applications and over-reliance on traditional Web Application Firewalls (WAFs). Advanced Persistent Threats (APTs) make overwhelming use of web attacks during infiltration and expansion phase. Noteworthy research has been carried out to detect web attacks using deep learning because traditional approaches fail against complicated attacks having crafted payloads, scripts and cookie manipulations. This paper proposes a framework based on an enhanced hybrid approach where Deep Learning model is nested with a Cookie Analysis Engine for web attacks detection, mitigation and attacker profiling in real time. We first generated a huge dataset over a period of time and trained our Convolution Neural Network (CNN) based deep learning model using Hypertext Transfer Protocol (HTTP) request parameters like Type, Content length, Data and Requested URL etc. We also developed a Cookie Analysis Engine that checks all incoming cookie(s) for integrity, mutations and failed sanitization checks and informs the user about probable privacy infringement by third party cookies. The framework analyzes the cascading output from the classifier and cookie analysis engine and takes the final decision. We performed rigorous testing of the proposed framework wherein it was first validated on our own custom dataset giving an accuracy of 99.94%. It was also validated on a publicly available benchmark dataset and gave an accuracy of 98.74%. When deployed in a controlled real time environment, the attacker profiling feature enabled the framework to save useful processing time as the deep learning classifier does not get triggered for every incoming request. This makes it easy to deploy in any environment to protect web applications in real time.

An Analysis of Computational Resources of Event-Driven Streaming Data Flow for Internet of Things: A Case Study

Abstract Information and communication technologies backbone of a smart city is an Internet of Things (IoT) application that combines technologies such as low power IoT networks, device management, analytics or event stream processing. Hence, designing an efficient IoT architecture for real-time IoT applications brings technical challenges that include the integration of application network protocols and data processing. In this context, the system scalability of two architectures has been analysed: the first architecture, named as POST architecture, integrates the hyper text transfer protocol with an Extract-Transform-Load technique, and is used as baseline; the second architecture, named as MQTT-CEP, is based on a publish-subscribe protocol, i.e. message queue telemetry transport, and a complex event processor engine. In this analysis, SAVIA, a smart city citizen security application, has been deployed following both architectural approaches. Results show that the design of the network protocol and the data analytic layer impacts highly in the Quality of Service experimented by the final IoT users. The experiments show that the integrated MQTT-CEP architecture scales properly, keeps energy consumption limited and thereby, promotes the development of a distributed IoT architecture based on constraint resources. The drawback is an increase in latency, mainly caused by the loosely coupled communication pattern of MQTT, but within reasonable levels which stabilize with increasing workloads.

Threat detection in Internet of Things using Cuckoo search Chicken Swarm optimisation algorithm

ABSTRACT Smart devices and people existing on the internet are connected to smart objects or things in the Internet of Things (IoT) technology. To protect the user information, it is required to detect malicious actions in the IoT environment. Even though different threat detection methods are introduced in the IoT technology, detecting malicious activity is still a significant challenge in the communication network. Hence, in this research work, an effective Cuckoo Search Chicken Swarm (CSCS) optimisation algorithm is proposed to detect the malicious threat in the network effectively. At first, the user activity information is simulated from the IoT network and stored in the user activity log. The user activity log file is forwarded to the feature extraction module, where the features, like logon, device, file, email, and Hypertext Transfer Protocol (HTTP) are extracted using the window length. For each user, the features are extracted with respect to the time stamp. Then, the dynamic feature index is constructed, and the threat detection is performed using the deep Long Short-Term Memory (LSTM) classifier, which is trained using the proposed CSCS algorithm. The proposed CSCS algorithm is designed by integrating the Cuckoo Search (CS) algorithm and the Chicken Swarm Optimisation (CSO) algorithm. Moreover, the proposed algorithm attained better performance with respect to the metrics, like namely F1-score, precision, and recall as 0.915, 0.975, and 0.884 by varying the k-value and 0.9286, 0.9235, and 0.9337 by varying the training data using window size as 10, respectively.

Design and implementation of an open-Source IoT and blockchain-based peer-to-peer energy trading platform using ESP32-S2, Node-Red and, MQTT protocol

An open-source P2P energy trading platform facilitates energy trading amongst the peers. The proposed system provides real time data acquisition, monitoring and control of self-generated energy at a remote location. The trading activities are done on a web interface that uses a private Ethereum blockchain. A smart contract is deployed on the Ethereum blockchain and the trading activities performed on the web interface are recorded on a tamper-proof blockchain network. An internet of things platform is used to monitor and control the self-generated energy. Energy data is collected and processed by means of ESP32-S2 microcontrollers using field instrumentation devices which are connected to the voltage source and load. An open-source decentralized Peer-to-Peer (P2P) energy trading system, designed on the blockchain and internet of things (IoT) architecture is proposed. The hardware setup includes a relay, a current sensor, a voltage sensor, a Wi-Fi router and ESP32-S2 microcontroller. For data transfer the Message Queuing Telemetry Transport (MQTT) protocol is used over a local network. ESP32-S2 is set up as MQTT client and Node-Red IoT server is used as MQTT broker. Hypertext Transfer Protocol (http) request method is implemented to connect the Node-Red server with the web interface developed using React.JS library. The system design, implementation, testing, and results are presented in this paper.

Virtual Private Network Flow Detection in Wireless Sensor Networks Using Machine Learning Techniques

Background: Every organization generally uses a VPN service individually to bypass the filters that hide the actual communication. Such communication filtration is not allowed by the organizational monitoring network. But these institutes are not in a position to spend a considerable amount of funds on a secure sockets layer to monitor traffic flow over their computer networks. Objective: Our work suggests a simple technique to block or detect annoying VPN clients inside the network activities. This method does not require the network to decrypt or even decode any network communication. Methods: The proposed solution selects two machine learning techniques Feature Tree and K-means as classification techniques that work on time-related features. First, the DNS mapping with the ordinary characteristic of the transmission control protocol / Internet protocol computer the network stack is identified, and it is not to be considered as a regular traffic flow if the domain name information is not available. The process not only examines non-standard utilization of hypertext transfer protocol security but also conceals such communication from hypertext transfer protocol security dependent filters in the firewall to detect as an anomaly in large. Results: We define the traffic flow as normal traffic flow and VPN traffic flow. These two flows are characterized by taking two machine learning techniques, Feature Tree and K-means. We executed each experiment 4 times. As a result, eight types of regular traffics and eight types of VPN traffics were represented. Conclusion: Once the traffic flow is identified, it is classified and studied by machine learning techniques. Using time-related features, the traffic flow is defined as normal flow or VPN traffic flow.

Design and analysis of a fair, efficient and stable multi-server adaptive streaming protocol

Due to the surging bandwidth demand for applications like 360° video streaming, hypertext transfer protocol (HTTP) adaptive video streaming has been extended to fetch contents from a single server to multiple servers. However, when players share a bottleneck link, it fails to hold three critical properties: fairness, efficiency, and stability. Effective algorithms have been proposed to tackle the problem in the single-server case. We revisit the three properties and show that they degenerate in multi-server streaming. In this paper, we propose MultiFast, a protocol that offers the best user experience by achieving fair, efficient, and stable multi-server video streaming. MultiFast consists of (1) a bitrate selection algorithm that ensures fairness with concurrent connections; (2) a connection management algorithm that ensures efficiency and stability by managing connections based on network conditions. Our real-world experiments show that MultiFast achieves the best performance in fairness, efficiency, and stability metrics, outperforming its closest alternative.

An Intelligent Information Management System for Retinal Image Storage and Recognition in Chronic Disease using Digital Signal and Image Processing

Retinal imaging plays a very significant role in the study of retinal vasculature changes indicative of chronic disease information related to vision. This study investigates the invulnerability of retinal image information in the disease information system and assesses the quantitative method of the morphological changes in the retinal vascular network. In this work, the medical digital image transmission protocol Digital Imaging & Communications in Medicine (DICOM) version 3.0 and the retinal image Picture Archiving & Communication System (PACS) were constructed in the laboratory using browser/server mode. Also, the DICOM-SR document was designed in this article using a list or hierarchy, and the retinal images to report the information of patients by using the Hypertext Transfer Protocol (HTTP) – based Web Access to DICOM Persistent Objects (WADO) approach. The results showed that the retinal image PACS system constructed in Browser/Server mode can effectively store and transmit DICOM images. When the imaging device is combined with the application program, special adapters are used to negotiate the transmission syntax. The message flow is decoded in the communication process, which can be connected with the realization to improve the efficiency of information collection. The proposed PACS system integrates the quantitative features of retina providing more meaningful research data for data mining in comparison to the traditional state of the art methods based on chronic disease management system. The diagnostic ability of the retinal imaging procedure using the DICOM images is justified by obtaining 98.51%, 98.04%, 99% and 99.01% of accuracy, sensitivity, specificity and precision values respectively.

Content dependent spatial resolution selection for MPEG DASH segmentation

The need for standardization emerged with the fast development of video streaming technologies. MPEG Dynamic Adaptive Streaming over Hypertext Transfer Protocol (MPEG DASH) is a standard for adaptive video streaming. Applications developed in compliance with MPEG DASH ensure smooth playback to end-users while being hardware agnostic, cost-efficient, and easy to maintain. The client side of the MPEG DASH system houses the adaptation logic which has been investigated intensively over the last several years. The server side stores representation sets of video sequences as well as accompanying Media Presentation Description and initialization file. Compared to the client side, the server side has not been yet sufficiently explored.Although there are some papers investigating the parameter selection for representation sets, most solutions are proprietary, do not take into account the content, and do not give the comprehensive methodology and its notation that can be applied to other codecs and encoder parameters. Also, most of the currently available solutions are costly and require large computational power. This article provides a comprehensive analysis and methodology for selecting the most appropriate spatial resolution switching points, which define the bitrates where switching to the higher spatial resolution is optimal. Furthermore, it presents a spatial resolution selection model for the estimation of switching points, which considers the spatial and temporal activity of video sequences. With the developed model, the optimal representation selection process is simplified and the segmentation process in MPEG DASH is improved. The methodology proposed for the development of this content-dependent spatial resolution selection model can be used for encoders and coding parameters not covered by the presented research.Quality of the video sequences segmentation using the proposed switching points were tested using two different test cases for network conditions and two different adaptation algorithms: Basic Adaptation algorithm and Segment Aware Rate Adaptation algorithm. The streamed videos that used the proposed segmentation have achieved better SSIM (Structural Similarity Index) results in 83.33% of cases in comparison to the videos that used segmentation presented in the relevant literature.

Mobile Green E-Waste Management Systems using IoT for Smart Campus

This paper presents the design and development of mobile “green” electronic waste (e-waste) management systems using Internet of Things (IoT) for smart campus. The system uses Raspberry Pi 3 Model B v1.2 microcontroller for monitoring e-waste object detection, e-waste count, and bin percentage level, respectively. TensorFlow Lite application programming interface (API) is used to run Single Shot Multibox Detector (SSD)Lite-MobileNet-v2 model trained on Microsoft Common Objects in Context (MSCOCO) dataset for e-waste object detection in image. All the monitoring data are stored and retrieved in ThingSpeak cloud platform using Hypertext Transfer Protocol (HTTP) and Message Queuing Telemetry Transport (MQTT) protocol over the Internet and displayed via interactive Android-based mobile user interface (UI). Furthermore, automatic e-mail notification will be sent to waste collector for bin collection whenever e-waste bin percentage level is greater than predetermined threshold value of 80% full.

Secure Browsing in Local Government: The Case of Portugal

This article addresses the adoption and use of Hypertext Transfer Protocol Secure (HTTPS) in the entry pages of the official websites of all (308) Portuguese municipalities. This is relevant because such websites are typically used to provide transactional services to citizens, and citizens need to trust that websites are authentic and that confidentiality and integrity of the information exchanged is assured in the communication process. Automated and, whenever needed, manual analyses were used to investigate the entry pages. Specifically, we checked for the existence of an HTTPS site; the correctness of website certificates and their certification chain; coherence between contents of the HTTP and HTTPS versions of websites; redirection from the HTTP version of a website to its HTTPS version; the existence of resources fetched using HTTP in HTTPS versions of websites; and exploitation of HSTS. A Quality Indicator was then defined and a classification of the municipalities into quality groups was produced. Possible determinants for the results obtained by the municipalities were also investigated. The general conclusion is that there is still much to be done to assure that citizens can communicate securely with the websites of all Portuguese municipalities, since only 3.6% of the municipalities were considered good, while 46.1% do not guarantee the minimum conditions. We argue that these results are associated with the fact that most Portuguese municipalities do not have the critical technical and managerial mass to correctly implement and maintain their websites. To mitigate this limitation, we propose the dissemination of technical instructions on how to correctly configure and deploy municipal HTTPS websites and the creation of shared services between the smaller municipalities.

A Deep Learning-Based Data Minimization Algorithm for Fast and Secure Transfer of Big Genomic Datasets

In the age of Big Genomics Data, institutions such as the National Human Genome Research Institute (NHGRI) are challenged in their efforts to share volumes of data between researchers, a process that has been plagued by unreliable transfers and slow speeds. These occur due to throughput bottlenecks of traditional transfer technologies. Two factors that affect the efficiency of data transmission are the channel bandwidth and the amount of data. Increasing the bandwidth is one way to transmit data efficiently, but might not always be possible due to resource limitations. Another way to maximize channel utilization is by decreasing the bits needed for transmission of a dataset. Traditionally, transmission of big genomic data between two geographical locations is done using general-purpose protocols, such as hypertext transfer protocol (HTTP) and file transfer protocol (FTP) secure. In this paper, we present a novel deep learning-based data minimization algorithm that 1) minimizes the datasets during transfer over the carrier channels; 2) protects the data from the man-in-the-middle (MITM) and other attacks by changing the binary representation (content-encoding) several times for the same dataset: we assign different codewords to the same character in different parts of the dataset. Our data minimization strategy exploits the alphabet limitation of DNA sequences and modifies the binary representation (codeword) of dataset characters using deep learning-based convolutional neural network (CNN) to ensure a minimum of code word uses to the high frequency characters at different time slots during the transfer time. This algorithm ensures transmission of big genomic DNA datasets with minimal bits and latency and yields an efficient and expedient process. Our tested heuristic model, simulation, and real implementation results indicate that the proposed data minimization algorithm is up to 99 times faster and more secure than the currently used content-encoding scheme used in HTTP of the HTTP content-encoding scheme and 96 times faster than FTP on tested datasets. The developed protocol in C# will be available to the wider genomics community and domain scientists.

A novel framework for delivering static search capabilities to large textual corpora directly on the Web domain: an implementation for Migne’s Patrologia Graeca

PurposeThis study aims to provide a system capable of static searching on a large number of unstructured texts directly on the Web domain while keeping costs to a minimum. The proposed framework is applied to the unstructured texts of Migne’s Patrologia Graeca (PG) collection, setting PG as an implementation example of the method.Design/methodology/approachThe unstructured texts of PG have automatically transformed to a read-only not only Structured Query Language (NoSQL) database with a structure identical to that of a representational state transfer access point interface. The transformation makes it possible to execute queries and retrieve ranked results based on a specialized application of the extended Boolean model.FindingsUsing a specifically built Web-browser-based search tool, the user can quickly locate ranked relevant fragments of texts with the ability to navigate back and forth. The user can search using the initial part of words and by ignoring the diacritics of the Greek language. The performance of the search system is comparatively examined when different versions of hypertext transfer protocol (Http) are used for various network latencies and different modes of network connections. Queries using Http-2 have by far the best performance, compared to any of Http-1.1 modes.Originality/valueThe system is not limited to the case study of PG and has a generic application in the field of humanities. The expandability of the system in terms of semantic enrichment is feasible by taking into account synonyms and topics if they are available. The system’s main advantage is that it is totally static which implies important features such as simplicity, efficiency, fast response, portability, security and scalability.

An automated model for the assessment of QoE of adaptive video streaming over wireless networks

Nowadays, heterogeneous devices are widely utilizing Hypertext Transfer Protocol (HTTP) to transfer the data. Furthermore, HTTP adaptive video streaming (HAS) technology transmits the video data over wired and wireless networks. In adaptive technology services, a client’s application receives a streaming video through the adaptation of its quality to the network condition. However, such a technology has increased the demand for Quality of Experience (QoE) in terms of prediction and assessment. It can also cause a challenging behavior regarding subjective and objective QoE evaluations of HTTP adaptive video over time since each Quality of Service (QoS) parameter affects the QoE of end-users separately. This paper introduces a methodology design for the evaluation of subjective QoE in adaptive video streaming over wireless networks. Besides, some parameters are considered such as video characteristics, segment length, initial delay, switch strategy, stalls, as well as QoS parameters. The experiment’s evaluation demonstrated that objective metrics can be mapped to the most significant subjective parameters for user’s experience. The automated model could function to demonstrate the importance of correlation for network behaviors’ parameters. Consequently, it directly influences the satisfaction of the end-user’s perceptual quality. In comparison with other recent related works, the model provided a positive Pearson Correlation value. Simulated results give a better performance between objective Structural Similarity (SSIM) and subjective Mean Opinion Score (MOS) evaluation metrics for all video test samples.

An enhanced authentication scheme for Internet of Things and cloud based on elliptic curve cryptography

SummaryAuthentication plays a fundamental role in achieving security over unreliable channels. Authentication is a technique that lets an entity verify the identity of other communicating entities. There are a plethora of authentication schemes available in recent times. A short time ago, Kumari et al. offered an authentication protocol using elliptic curve cryptography (ECC) for Internet of Things (IoT) and professed that their design defends various security attacks. We examined Kumari et al.'s scheme and detected some loopholes like (1) inefficient login phase, (2) inefficient authentication phase, (3) known session‐specific temporary information attack, (4) denial‐of‐service (DoS) attack, and (5) lack of password changing phase. To eradicate all these flaws, we present an enhanced authentication protocol utilizing ECC for deployment in the field of IoT using cloud servers. This protocol uses hypertext transfer protocol (HTTP) cookies to authenticate clients. The developed protocol provides various security features and able to counter various security attacks. The security analysis validates that the extended scheme is potent. We simulate our protocol in Automated Validation of Internet Security Protocols and Applications (AVISPA) tool that evaluates the safety of the scheme in the presence of an adversary. We compare the proposed scheme with some similar schemes in respect of computation cost, estimated time, storage cost, communication cost, and security features.