Client-server Research Articles

LOGISTICS SERVICE INFORMATION SYSTEM AUDIT USING COBIT 5 FRAMEWORK

KBN Prima Logistik is a subsidiary affiliated with PT. Kawasan Berikat Nusantara which operates in the field of logistics services. In carrying out its business processes, it has implemented an Information System in the form of using desktop-based supporting business applications (Client Server). Some of the current problems are that the applications used are not functioning optimally, IT Governance procedures are not well defined by standards, there is no IT Division in the Organizational Structure that is responsible for IT management and there is a shortage of competent human resources in the IT field. Therefore, it is necessary to measure the Maturity Level and Capability Level of IT governance. So, research was carried out using the COBIT 5 framework as an Information System Audit model. The research method goes through the stages of problem identification, literature study, and domain determination which focuses on the DSS and MEA domains. The data collection method was through interviews and distributing questionnaires filled in by 20 respondents. The research results show that the Maturity Level value in the DSS and MEA domains shows an average value of 1.94 or 194%. Capability Level is still at Level 2 (Managed Process) from the expected Target Level 3 (Established Process). This indicates that although the IT processes has been run and implemented regularly with planning and monitoring according to business process objectives, but the management is not yet optimal and not well standardized. The results of the gap assessment show that the average gap value is 1.06.

A Novel Charging Management and Security Framework for the Electric Vehicle (EV) Ecosystem

The EV charging network has witnessed significant growth in the UK in the last few years due to the net zero emission target of the government by 2030. The related literature in EV charging management mainly focuses on road-traffic-parameter-based optimization and lacks detail in terms of charging statistics and cyber–security-enabled charging management frameworks. In this context, this paper proposes a novel EV Charging Management and Security (EVCMS) framework using real-time charging statistics and an Open Charge Point Protocol (OCPP). Specifically, a system model for EVCMS is presented considering charging data management and security protocols. An EVCMS framework design is detailed, focusing on charging pricing, optimization, and charging security. The experimental implementation is described in terms of client–server and charge-box-based simulation. The performance of the proposed EVCMS framework is evaluated by considering different charging scenarios and a range of charging-related metrics. An analysis of results and comparative study attest to the benefits of the proposed EVCMS framework for enabling the EV charging ecosystem.

Developing information technology for evaluating and enhancing application-layer DDoS attack detection methods

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.

Evaluation of a New-Concept Secure File Server Solution

Ransomware attacks are becoming increasingly sophisticated, and defensive solutions must necessarily evolve. Unfortunately, automated backup management through validation—critical for data recovery after an attack—and strengthening file server protection are not sufficiently addressed in current protection strategies. To focus on this area, an architectural model has been developed that complements traditional central data storage with an additional high-level file server protection solution. However, in addition to the functional adequacy of file server protection solutions, the measurability of operational performance is also important. In order to validate and evaluate the developed ransomware-proof file server protection, a dynamic performance metric is introduced for comparability, by which the performance results measured on the tested client–server architectures are presented, together with the methodology and results of the measurements. Our results show that the investigated model does not cause any performance degradation when moving sensitive data files and their backups during operation and even shows performance improvements in some frequently used configurations. The result proves that the developed real-time approach provides a solution to this critical problem in terms of the time required to restore key data from backups and to ensure file availability and continuity of accessibility. Based on a review of the literature and available solutions, it is concluded that there is no integrated solution for implementing a similar concept in practice, and therefore, the developed model is a gap-filling in this priority area.

Designing transport scheme of 3D naked-eye system

3D naked-eye is constructed from multi-stream as a typical representation of stereoscopic video. Its enormous data volume and stringent low-delay transport requirements pose significant challenges for high-quality real-time transport. Through analysis and experimental verification that current streaming media transport frameworks using the server–client or peer-to-peer scheme face difficulties when transmitting 3D naked-eye in a one-to-one format. Besides, the existing bandwidth estimation algorithms cannot achieve the expected performance when dealing with delay-sensitive traffic. This results in low bandwidth utilization and slow bandwidth estimation, rendering it unfeasible to deliver multi-stream on time. We propose an effective transport framework with different modules for real-time multi-stream and introduce an Agent-to-Agent transport scheme that provides many-to-one connection as the main implementation way of 3D naked-eye transport framework. Additionally, we propose a direct bandwidth estimation algorithm to quickly match network bandwidth for low-delay transport. The Agent terminal centrally processes consolidates transports, and provides macro-level management of multiple video streams. The algorithm directly detects the available bandwidth using packet interval and packet rate models. Finally, using rate decision algorithm arbitrates the results to directly measure the maximum available bandwidth of the link. The Agent-to-Agent achieves 99% bandwidth utilization, addressing the limitations of existing streaming schemes in handling concurrent data streams. Our algorithm provides precise bandwidth estimates with minimal time overhead, meeting the requirements of a delay-sensitive 3D naked-eye system across diverse environments.

EXTENDED SERIAL GRAPH-VALIDATION QUEUE SCHEME WITH LOCKING STRATEGY

In today's digital landscape, collaborative work in real-time is on the rise, allowing individuals to connect across different locations through applications facilitated by client-server architecture, enabling users to access and work on the same project simultaneously. However, clients' simultaneous access and modifications to the database can result in data inconsistencies, underscoring the importance of concurrency control. Managing concurrent transactions can introduce complexities and potentially adversely impact server performance. Object caching emerges as a viable solution as an alternative approach to handling transaction traffic. Extended Serial Graph-Validation Queue (Extended SG-VQ) is a control concurrency scheme that operates within the client-server architecture framework and incorporates object caching. The cache component implements a queue-based validation algorithm as part of its validation process. At the same time, the server-side employs a graph-based validation algorithm with locking strategies. Through a series of hypothetical transaction scenarios across three cases, this study validates the effectiveness of the Extended SG-VQ, demonstrating its ability to utilize serial graphs, resolve conflicts, and identify cyclic patterns.

Self-adaptive asynchronous federated optimizer with adversarial sharpness-aware minimization

The past years have witnessed the success of a distributed learning system called Federated Learning (FL). Recently, asynchronous FL (AFL) has demonstrated its potential in concurrency compared to mainstream synchronous FL. However, the inherent systematic and statistical heterogeneity has presented several impediments to AFL: On the client side, the discrepancies in trips and local model drift impede global performance enhancement; On the server side, dynamic communication leads to significant fluctuations in gradient arrival time, while asynchronous arrival gradients with ambiguous value are not fully leveraged. In this paper, we propose an adaptive AFL framework, ARDAGH, which systematically addresses the aforementioned challenges: Firstly, to address the discrepancies in client trips, ARDAGH ensures their convergence by incorporating only 1-bit feedback information into the downlink. Secondly, to counter the drift of clients, ARDAGH generalizes the local models by employing our novel adversarial sharpness-aware minimization, which does not necessitate reliance on additional global variables. Thirdly, in the face of gradient latency issues, ARDAGH employs a communication-aware dropout strategy to adaptively compress gradients to ensure similar transmission times. Finally, to fully unleash the potential of each gradient, we establish a consistent optimal direction by conceptualizing the aggregation as an optimizer with successive momentum. In light of the comprehensive solution provided by ARDAGH, an algorithm named FedAMO is derived, and its superiority is confirmed by experimental results obtained under challenging prototype and simulation settings. Particularly in typical sentiment analysis tasks, FedAMO demonstrates an improvement of up to 5.351% with a 20.056-fold acceleration compared to conventional asynchronous methods.

2FAKA-C/S: A Robust Two-Factor Authentication and Key Agreement Protocol for C/S Data Transmission in Federated Learning

As a hot technology trend, the federated learning (FL) cleverly combines data utilization and privacy protection by processing data locally on the client and only sharing model parameters with the server, embodying an efficient and secure collaborative learning model between clients and aggregated Servers. During the process of uploading parameters in FL models, there is susceptibility to unauthorized access threats, which can result in training data leakage. To ensure data security during transmission, the Authentication and Key Agreement (AKA) protocols are proposed to authenticate legitimate users and safeguard training data. However, existing AKA protocols for client–server (C/S) architecture show security deficiencies, such as lack of user anonymity and susceptibility to password guessing attacks. In this paper, we propose a robust 2FAKA-C/S protocol based on ECC and Hash-chain technology. Our security analysis shows that the proposed protocol ensures the session keys are semantically secure and can effectively resist various attacks. The performance analysis indicates that the proposed protocol achieves a total running time of 62.644 ms and requires only 800 bits of communication overhead, showing superior computational efficiency and lower communication costs compared to existing protocols. In conclusion, the proposed protocol securely protects the training parameters in a federated learning environment and provides a reliable guarantee for data transmission.

Comprehensive Review on Machine Learning Applications in Cloud Computing

Cloud computing provides on-demand access to a variety of processing, storage, and network resources. Over the past few years, cloud computing has become a widely accepted computing paradigm and one of the fastest-growing model in the IT industry. It turns out to be a new computing evolution after the evolution of mainframe computing, client-server computing and mobile computing. Cloud computing model faces various challenges such as security, resource allocation, load balancing, incast, interoperability. Machine learning is the study of computer algorithms that get better on their own via experience. Algorithms for machine learning are strong analytical techniques that let computers see patterns and help people learn. In this review paper, we present an analysis of various cloud computing issues and machine learning algorithms. Furthermore, we have comprehensively analyzed applications of numerous machine learning algorithms that are used to mitigate a variety of cloud computing issues.

A Multimodal Robotic Communication System for Students with Profound and Multiple Disabilities

Different researches claim that using a robot toplay with students with profound and multiple disabilitiesmight be a valuable tool to increase the learning curve ofthose students. Robots in schools and in other educationalenvironments is a growing research field and it seems that it is time for technology to transform the education withpeople of special needs similarly to how technology affectedmainstream education. The project focuses on usingdifferent switches plugged in a joybox to send signals toNAO Robot. Each switch caries a certain behavior. JoyBox contains eight numbered connectors and four directionconnectors to move the robot. The paper implements clientserver communication to connect to the robot and be able tosend signals to the robot via the switches connected to theJoyBox. Using the switch enables the users to communicate to NAO independently. This gauntness a deep level ofengagement with the robot, since the robot moves based onthe singles received from the switches. The paper describesthe setup of the client server communication, and the robotknows which behaviour to implement next. This study implements a case study evaluated by only four researchersand staff from Oakfield school to evaluate the software .Thisstudy hopes to be able to assess the needs of school staff toimprove communicational methods for students withprofound and multiple disabilities.

RESEARCH ON SOFTWARE FOR ERROR PROBABILITY PREDICTION IN BUSINESS PROCESS MODELS USING LOGISTIC REGRESSION

Business process modeling allows to graphically represent organizational activities and related events. It allows to identify areas for improving the organizational performance, define requirements for software solutions, and, in general, to facilitate communication between IT and business parties within or between different organizations. Therefore, at the stage of representing the activity in the form of a model, it is necessary to understand how likely it is that errors will occur during the implementation of the depicted business process. Thus, this study aims to improve the quality of business process models by solving the problem of predicting the error probability of business process execution. In order to assign error probabilities to each business process model from the training dataset, it is proposed to use one of the complexity metrics – the coefficient of network connectivity. To predict the error probability in business process execution, it is proposed to use the simplest and most intuitive machine learning model – logistic regression. As independent variables, it is proposed to choose the basic metrics of business process modeling – the number of nodes and arcs. Thus, the algorithm for solving the task includes steps related to calculating probabilities for the training data set, preparing training and test sets, determining regression parameters, visualization, and evaluation of training results. For the software that implements the proposed approach, a client-server architecture was chosen due to its flexibility and scalability. When developing software components, the Scikit-Learn machine learning library and the Python programming language were used to build a logistic regression mathematical model. The software tool is implemented as a web application based on MySQL, the Node JS platform, and the Express JS web framework. The quality assessment results of the developed prediction model indicate the suitability of the software tool for solving the problem of predicting the error probability of business process execution.

Automate the verification of session cookie attributes

In this research, we focus on a critical web security topic, namely the security of session cookies, which play a key role in the functioning of modern web applications. As a standard mechanism for storing data on the client side, cookies are crucial for authentication, authorization and maintaining the state of a user's session. However, despite their necessity and convenience, cookies can also pose serious security risks. Our research focuses on the analysis and automation of cookie attribute verification, which is critical to ensuring protection against various web attacks. Identifying and eliminating weaknesses in cookie attributes can significantly reduce the risk of malicious attacks such as session hijacking, cross-site scripting (XSS) and cross-site request forgery (CSRF) attacks. We take an in-depth look at modern methods and tools for securing cookies, including implementing strict policies on cookie attributes such as Secure, HttpOnly, and SameSite. These attributes help to restrict access to cookies from unauthorized use via client-side scripts and provide additional protection against cross-site attacks. In addition, we consider the importance of updating the cookie standard, RFC6265bis, which offers improved security mechanisms, including the SameSite attribute, which allows controlling the sending of cookies during cross-requests, thereby reducing the risk of CSRF attacks. Our research also includes an analysis of potential threats and vulnerabilities associated with the misuse or misconfiguration of cookies, as well as a discussion of strategies to minimize these risks. We demonstrate how detailed automated verification of cookie attributes can significantly improve the security of web applications. The results of the study point to the need to constantly monitor and evaluate the protection of session cookies, as well as the importance of implementing security best practices and standards to ensure the reliability and security of web applications.

Software Architecture and Desgin for Online Registration System

This paper presents an example of SoftwareArchitecture and design for online registration system.Software design is an early phase of the Software DevelopmentLife Cycle (SDLC). During this phase, software designersmodel the system and assess its quality so that improvementsmay be made before the software goes into the productionphase. The goal of software design is to build a model thatmeets all customer requirements and leads to successfulimplementation. As software systems continue to grow inscale, complexity, and distribution, their proper designbecomes extremely important in software production. Thearchitecture design representat ion is derived from the systemrequirement specification and the analysis model. Multi tierarchitecture is commonly used for distributed systems. Itusually consists of three element types: client, middlewareserver, and data server. The Unified Modeling Language( used to model the problem domain; describe the userrequirements; identify significant architecture elementsduring software design, such as classes and objects; describebehavior and interactions among these elements; and organizethe softw are structure, specify its constraints, describe thenecessary attributes, and more.

Decentralized IoT data sharing: A blockchain-based federated learning approach with joint optimizations for efficiency and privacy

Blockchain-based Federated Learning (BCFL) is gaining significant attention as a promising decentralized data sharing technology with privacy protection. Most existing BCFL frameworks loosely couple blockchain and Federated Learning (FL). FL transforms data sharing into model sharing, while blockchain decentralizes the model aggregation and handles security verification. However, this simplistic overlay of the two technologies often involves third-party blockchain peers, leading to inefficient workflows and potential privacy attacks from malicious blockchain peers. Some studies have proposed differential privacy with noise addition to prevent privacy attacks, yet most do not allow clients to customize privacy budgets, impacting data availability and limiting BCFL’s application in data sharing. To address these challenges, we first introduce a novel tightly-coupled BCFL framework that integrates training and mining at the client side. Under this framework, a totally decentralized data sharing process is established. Moreover, a Personalised Differential Privacy (PDP) mechanism is devised, enabling clients to add Laplace noise to model gradients based on custom privacy budgets. To achieve optimal privacy budgets, a privacy optimization mechanism based on Stackelberg games is proposed. It establishes utility functions for data requesters and providers, models the process of utility optimization as a Stackelberg game process, and obtains the optimal privacy budget while maximizing utility for all participants. This facilitates flexible and on-demand privacy-protected data sharing. Extensive experiments validate the effectiveness of our approach in enhancing system efficiency and facilitating flexible on-demand privacy-protected data sharing, further solidifying BCFL’s potential in decentralized data sharing scenarios.

Amber free energy tools: Interoperable software for free energy simulations using generalized quantum mechanical/molecular mechanical and machine learning potentials

We report the development and testing of new integrated cyberinfrastructure for performing free energy simulations with generalized hybrid quantum mechanical/molecular mechanical (QM/MM) and machine learning potentials (MLPs) in Amber. The Sander molecular dynamics program has been extended to leverage fast, density-functional tight-binding models implemented in the DFTB+ and xTB packages, and an interface to the DeePMD-kit software enables the use of MLPs. The software is integrated through application program interfaces that circumvent the need to perform “system calls” and enable the incorporation of long-range Ewald electrostatics into the external software’s self-consistent field procedure. The infrastructure provides access to QM/MM models that may serve as the foundation for QM/MM–ΔMLP potentials, which supplement the semiempirical QM/MM model with a MLP correction trained to reproduce ab initio QM/MM energies and forces. Efficient optimization of minimum free energy pathways is enabled through a new surface-accelerated finite-temperature string method implemented in the FE-ToolKit package. Furthermore, we interfaced Sander with the i-PI software by implementing the socket communication protocol used in the i-PI client–server model. The new interface with i-PI allows for the treatment of nuclear quantum effects with semiempirical QM/MM–ΔMLP models. The modular interoperable software is demonstrated on proton transfer reactions in guanine-thymine mispairs in a B-form deoxyribonucleic acid helix. The current work represents a considerable advance in the development of modular software for performing free energy simulations of chemical reactions that are important in a wide range of applications.

FedREAS: A Robust Efficient Aggregation and Selection Framework for Federated Learning

In the field of Natural Language Processing (NLP), Deep Learning (DL) and Neural Network (NN) technologies have been widely applied to machine translation and sentiment analysis and have demonstrated outstanding performance. In recent years, NLP applications have also combined multimodal data, such as visual and audio, continuously improving language processing performance. At the same time, the size of Neural Network models is increasing, and many models cannot be deployed on devices with limited computing resources. Deploying models on cloud platforms has become a trend. However, deploying models in the cloud introduces new privacy risks for endpoint data, despite overcoming computational limitations. Federated Learning (FL) methods protect local data by keeping the data on the client side and only sending local updates to the central server. However, the FL architecture still has problems, such as vulnerability to adversarial attacks and non-IID data distribution. In this work, we propose a Federated Learning aggregation method called FedREAS. The server uses a benchmark dataset to train a global model and obtains benchmark updates in this method. Before aggregating local updates, the server adjusts the local updates using the benchmark updates and then returns the adjusted benchmark updates. Then, based on the similarity between the adjusted local updates and the adjusted benchmark updates, the server aggregates these local updates to obtain a more robust update. This method also improves the client selection process. FedREAS selects suitable clients for training at the beginning of each round based on specific strategies, the similarity of the previous round’s updates, and the submitted data. We conduct experiments on different datasets and compare FedREAS with other Federated Learning methods. The results show that FedREAS outperforms other methods regarding model performance and resistance to attacks.

Design Ship Fuel Level Prototype Based on LoRa Dragino 915 MHz

Abstract The development of monitoring and control system technology in the shipping industry today is experiencing very rapid development, one of the developing technologies is the fuel monitoring system. The fuel monitoring system is very important in the shipping industry because it relates to shipping operational costs and controls fuel usage during the voyage. Currently, fuel level monitoring is carried out using a sensor that sends data using a cable this has a problem because sending data via cable will have noise which will affect the measurement results. In this study, it will be discussed sending data from sensor measurements without using cables using the LoRa Dragino 915 MHz module, where the LoRa module will be divided into two points, namely LoRa Client and LoRa Server. The results of this study, the ship’s fuel level reading system can function properly and according to the specifications expected. The results of this prototype can read the condition of the fuel level in the fuel tank in real-time. In addition, the communication range between LoRa Client and Lora Server tested in this study can work with a distance of 170 meters in an open space and has a communication distance of 63 meters in a closed room.

Intelligent Trash Level Detection for Sustainable Urban Waste Management

Abstract This paper introduces an advanced trash bin fill ratio detection system, addressing the challenges of municipal solid waste management. Utilising the ESP32 CAM module, the system captures and transmits bin images via the ESP MESH protocol, enhancing waste management efficiency. The hardware relies on sustainable energy sources, including a photovoltaic cell and LiPo battery, ensuring continuous operation. On the software front, a client-server architecture is employed. The client-side captures images, while the server-side processes these images using a Python script and SQL database to determine bin fill levels. This innovative approach offers a scalable, cost-effective solution for improving urban waste collection and management.

RRA-FFSCIL: Inter-intra classes representation and relationship augmentation federated few-shot incremental learning

Federated learning (FL), as a distributed machine learning paradigm, enables on-device model training and inference without data updates or privacy breaches, promoting edge intelligence in Industrial Internet of Things (IIoTs) applications. However, FL applying in IIoTs faces some challenges in practice: (1) the catastrophic forgetting of old data resulting from a streaming reception for arriving data; (2) the forgetting exacerbated by the non-i.i.d. data distribution across devices; (3) the overfitting caused by insufficient sample size of new data (i.e., few samples). To address the above problems, we propose an inter-intra classes representation and relationship augmentation federated few-shot incremental learning framework called RRA-FFSCIL, effectively mitigating catastrophic forgetting from local and global perspectives. Specifically, forgetting from streaming data is alleviated by designed modules in the client side of RRA-FFSCIL: a random few-shot (FS) episodic adaptation strategy, a class-projection relation strengthening loss function, and a class-aware distribution balancing loss function. Meanwhile, the mitigation of global forgetting from non-i.i.d. of distributions across devices thanks to selecting the best old models on the server to assist local class relation enhancement. We conduct extensive experiments on three datasets (cifar100, tinyImageNet, miniImageNet) to validate the effectiveness and superiority of RRA-FFSCIL in addressing the forgetting problem associated with streaming data.

A coin-based incentive model for curbing free riding in peer-to-peer networks

Peer-to-peer networks have emerged and evolved in their use due to the limitations of the centralised client-server network architecture. They are used in applications such as video games for text, images, sound, and video. Resource sharing in peer-to-peer networks has enhanced multimedia distribution, system scalability, and performance. Peer-to-peer systems depend on the unimpeded collaboration of peers on the network, leading to certain peers declining to provide resources to the network and being identified as free riders. Free riding has become a significant concern in peer-to-peer systems and requires attention. Free-riding activities impact the performance of a peer-to-peer video-on-demand system, reducing its efficiency. This study explores the utilisation of incentive mechanisms to address free-riding on peer-to-peer networks and suggests an effective overlay network for this purpose. The coin-based approach, an incentive mechanism, aims to mitigate free riding by rewarding participating peers with coins and distinguishing those who do not participate