Development Of Internet Research Articles

Pemanfaatan Internet of Thing Dalam Dunia Pendidikan Sekolah Menegah Atas Negeri 1 Kepahiang

The relationship between technology and education has been going on for quite a long time. The use of information and communication technology for learning is increasingly developing. With the idea of ​​developing the internet of things (IoT), the use of technology for learning is increasingly showing significant progress. As a consequence, life activities are very dependent on the Internet, one of which is carrying out the education process. Considering the enormous potential for development of Internet of Things (IoT) technology, knowledge about this technology and how to use it in everyday life is very necessary. SMAN 1 Kepahyang has development potential in utilizing IoT technology, but requires training in its use in education. For this reason, the Faculty of Computer Science, Dehasen University, Bengkulu conducted community service regarding IoT technology to students at SMAN 1 Kepahyang. Material related to IoT is provided in stages from introduction to practical use of IoT in everyday life. The results of the training show that the students gained knowledge about how to use IoT in education

New Hybrid Distributed Attack Detection System for IoT

IoT is expressed as a network of physical objects with applications and various technologies that provide data connection and sharing with various devices and systems over the Internet. Security vulnerabilities in IoT devices are one of the biggest security issues in connecting devices to the internet and collecting and processing user data. These vulnerabilities can lead to increased attacks on IoT devices and malicious use of user data. In this article, we discuss these security problems that arise in IoT systems in detail in distributed systems technology. Distributed systems are increasingly used in the modern computing world. These systems are a structure where multiple independent computers communicate with each other for a common purpose. Distributed system technologies have become more common with the development of internet and cloud computing systems. However, the use of distributed systems has brought with it important security challenges such as security vulnerabilities, access controls and data integrity issues. Therefore, the security of distributed system technologies has been an important focus of work in this area. In this study, information about distributed system technologies and security for IoT is given. The all attack types were classified using ANN, developed RF and hybrid model. In RF, all feature vectors created from all datasets (bank and two financial datasets) were also analyzed separately and the classification performance was examined. In addition, a new RF algorithm based on weight values using the Gini algorithm has been proposed. With this algorithm, the traditional RF algorithm has been developed and the success rates have been increased. In addition, a hybrid method was created by classifying the datasets obtained by RF with ANN. With the hybrid method ANN and the enhanced RF method, its accuracy in detecting normal behaviors and attack types was calculated and the success of the methods was presented comparatively. In addition, the working times of the methods were determined.

Learning Accurate and Bidirectional Transformation via Dynamic Embedding Transportation for Cross-Domain Recommendation

With the rapid development of Internet and Web techniques, Cross-Domain Recommendation (CDR) models have been widely explored for resolving the data-sparsity and cold-start problem. Meanwhile, most CDR models should utilize explicit domain-shareable information (e.g., overlapped users or items) for knowledge transfer across domains. However, this assumption may not be always satisfied since users and items are always non-overlapped in real practice. The performance of many previous works will be severely impaired when these domain-shareable information are not available. To address the aforementioned issues, we propose the Joint Preference Exploration and Dynamic Embedding Transportation model (JPEDET) in this paper which is a novel framework for solving the CDR problem when users and items are non-overlapped. JPEDET includes two main modules, i.e., joint preference exploration module and dynamic embedding transportation module. The joint preference exploration module aims to fuse rating and review information for modelling user preferences. The dynamic embedding transportation module is set to share knowledge via neural ordinary equations for dual transformation across domains. Moreover, we innovatively propose the dynamic transport flow equipped with linear interpolation guidance on barycentric Wasserstein path for achieving accurate and bidirectional transformation. Our empirical study on Amazon datasets demonstrates that JPEDET significantly outperforms the state-of-the-art models under the CDR setting.

Research on the Status Quo and Dilemma of Minors' Personal Information Protection

With the rapid development of mobile Internet, minors' personal information is inevitably collected and processed by various information processors. Due to the characteristics of immature mental development and weak risk prediction ability of minors, it is difficult to effectively protect their personal information security. Although China's "Personal Information Protection Law" makes provisions on the protection of minors' personal information, there are still some problems such as "one-size-fits-all" age limit and imperfect guardian consent system. This paper will test the current hundreds of various types of apps, conduct a questionnaire survey on the protection of personal information of minors and their guardians, analyze the current dilemma of the protection of personal information of minors at the social level, propose countermeasures according to the symptoms, and explore more targeted ways to protect the rights and interests of minors' personal information. Escort the physical and mental healthy development of minors under the background of the information age.

BDPM: A secure batch dynamic password management scheme in industrial internet environments

The rapid development of Industrial Internet has promoted the deep integration of Information Technology (IT) and Industrial Control (IC), so that network attacks have gradually invaded IC zone. Password security is the first line of defense to ensure the security of IC devices. In this paper, we propose a secure Batch Dynamic Password Management (BDPM) scheme in Industrial Internet environments. Aiming to automatically configure strong passwords for IC devices, our scheme can achieve a batch password generation algorithm based on SM3 Cryptographic Hash Algorithm, which encrypts the input string and then intercepts and replaces the hash value to ensure the uniqueness and crack resistance of passwords. Moreover, we continuously monitor the status of vulnerable IT devices through a zero trust anomaly monitoring mechanism and introduce a password updating mechanism for relevant IC devices, which is triggered by sending an alarm to IC devices that have interaction rights with the compromised IT device. Subsequently, we construct a resilient blockchain called PS_chain and execute two different password storage schemes based on the threshold of password updates to ensure storage security and reduce the load on block storage. The security analysis shows that our scheme can defend against the threat model and can comprehensively improve the security of IC device passwords. The simulation results show that our scheme can enhance the strength of IC device passwords while securely storing IC device passwords in a low-load manner.

An efficient deep learning mechanisms for IoT/Non-IoT devices classification and attack detection in SDN-enabled smart environment

In recent years, the development of Internet of Things (IoT) applications has increased, resulting in higher demands for sufficient bandwidth, data rates, latency, and quality of service (QoS). In advanced communications, managing network resources for allocating IoT services and identifying the exact IoT devices connected to a network is a major concern. The existing studies have introduced various methods for classifying IoT devices in a network. However, the previous studies faced challenges like limited attributes, low efficiency, inappropriate features, and computational complexities. Also, the existing studies failed to concentrate on IoT/Non-IoT classification along with attack detection. Detecting attacks on IoT devices is critical for making network services more effective. Thus, the proposed study introduces an efficient IoT device classification and attack detection mechanism using software defined networking (SDN)-enabled fiber-wireless access networks internet of things (FiWi IoT) architecture. Initially, an effective resource allocation process is performed to mitigate the delay constraint issues by introducing a hybrid parallel neural network-based dynamic bandwidth allocation (DBA) method. Then, the input traffic information is gathered from the resource-efficient SDN-enabled FiWi IoT network, and the input data is pre-processed to eliminate unwanted noises using min-max normalization and standardization. Next, the essential attributes are extracted to attain enhanced classification performance. To reduce the feature dimensionality problem and thereby solve complexity issues, the most optimal features are selected by a new chaotic seagull optimization (CSO) approach. After that, IoT/non-IoT classification is performed using a transformer-driven deep intelligent model. Finally, the attacks are detected and classified by introducing a novel slice attention-based deep capsule autoencoder (SA_DCAE) model. For experimentation, the Python 3.7.0 tool is used in this work, and the performance of proposed classifiers is measured by evaluating varied matrices. Also, the comparison analysis proves the superiority of the proposed techniques to other existing methods.

Enhancing user experience in home networks with machine learning-based classification

With the rapid development of mobile Internet, home broadband has been integrated into people's daily lives, and the market has become increasingly saturated. User experience and broadband quality have become the key factors determining market competitiveness, and consequently, most operators currently are increasing attention to network quality issues and how to improve user experience. This paper proposes an efficient machine learning model to accurately evaluate home user network experiences. The dataset used encompasses network indicator data from 500 anonymized users, and presents a set of formidable challenges including a non-standard sampling rate and time range, an uneven distribution of observations, multiple recorded observations for identical timestamps, a constrained sample size, a subjective definition of Internet experience, and a lack of essential information regarding the data collection setup. Our novel time series characteristic-based method extracts thousands of descriptive statistics from the time series sequences which reveal that, even in the face of the dataset's inherent complexities, our proposed method excels, achieving an impressive 67% validation accuracy. This represents a substantial 3% enhancement over the performance of conventional models on this dataset. Furthermore, we explore the potential of a Recurrent Neural Network (RNN) model, which also yields promising results with a validation accuracy of 58%. It is important to underscore that the performance of the RNN model could be substantially enhanced with a larger dataset. [...]

Construction and application of online parametric modeling platform for face milling cutters based on WebGL and Open CASCADE

The secondary development of traditional commercial CAD/CAE software has been widely used in the process of cutting tools design and optimization. However, limited by copyright and hardware configuration, the effect of this development is limited, and the secondary development can no longer meet the needs of rapid and accurate cutting tools design. With the rapid development of Internet, online cutting tools CAD software based on Web has become a new development direction with its characteristics of specialization and lightweight. In order to build an online cutting tools CAD software, an online parametric design platform for indexable face milling cutters is established based on Web development technology, Open CASCADE and Three.js. The platform can quickly generate a 3D basic model for NC machining programing and a 3D detailed model for finite element simulation of cutting process according to ISO 13399. Based on the built platform, the finite element simulation model of cutting process of indexable surface milling cutter is established. The establishment of the model can play an important role in improving the design efficiency and reducing the design cost for the optimal design of this kind of milling cutter.

The Evaluation of College Chinese Teaching Effect Based on Internet of Things Technology

Aiming at the problem of the evaluation of College Chinese teaching, based on the development of internet of things technology, this paper discusses the teaching rules of internet of things technology in detail and makes relevant analysis. Then, college students of different grades are selected and applied to the specific evaluation of College Chinese teaching based on internet of things technology. The experimental results show that college students of different grades have different effects on College Chinese teaching based on internet of things technology. In addition, in the process of College Chinese teaching, freshmen and sophomores have a positive evaluation of the effect of Chinese teaching and have a strong interest in Chinese learning while senior students have the worst evaluation and interest in learning. Finally, the paper analyzes the possible problems in the evaluation of College Chinese teaching based on internet of things technology.

Analisis Perbedaan Minat Beli Di Tokopedia.Com Dan Bukalapak.Com (Studi Kasus Konsumen Tokopedia.Com Dan Bukalapak.com Pada Perguruan Tinggi Di Lingkungan Pondok Pesantren Bahrul Ulum)

In this digital era, business has transformed significantly, using the internet as the primary tool. Sites such as Tokopedia.com and Bukalapak.com, which function as online marketplaces, facilitate sellers to reach buyers more efficiently. This study is directed at exploring and comparing the level of shopping interest in Tokopedia.com and Bukalapak.com. The internet's dramatic evolution in Indonesia has profoundly affected various dimensions of life, especially in the business realm. In this case, the role of individuals as active internet users enables free interaction with fellow users. In the context of interest, a critical element of forecasting buyer behavior, it reflects an individual's reaction to a particular product or service. This study utilizes descriptive and comparative methods, relying on primary and supplementary information. The non-probability and accidental sampling techniques were chosen to determine the research subjects. This study's findings show no notable disparity in purchase intention between Tokopedia.com and Bukalapak.com, a result that provokes businesses to sharpen their competitive tactics in the digital market arena. Keyword: Business Online, Buying of Interest, Market Place

A Comprehensive Survey on Recommender Systems Techniques and Challenges in Big Data Analytics with IOT Applications

Purpose: Purpose of this research is to carry out survey on Recommendation systems techniques in Big Data Analytics. This article presents designing of recommender systems and evaluates it with help of various performance metrics in IoT applications.
 
 Theoretical framework: With fast development and applications of Internet of Things, large amount of user data is generated and accumulated every day. Growth of media consumption in online social networks is exponential which requires an efficient and effective recommendation system to enhance excellence in experience for users. Recommender systems help users to overcome Information Overload problem by providing them relevant contents. 
 
 Method/design/approach: The main aspect of recommender system is how to take complete advantage of this ubiquitous data. Recommender system is mainly used to guess or predict users’ interests and make relevant recommendations. Collaborative filtering is the technique that uses the relationships between users and between items in order to build a prediction. Collaborative filtering algorithms are mainly categorized as model-based methods and memory-based methods. In this article, various methods to build recommender system are described. Similarly, Collaborative filtering uses Pearson cosine, cosine vector, Jaccard similarity to identify same users or items. Recommender system has various applications in domain such as healthcare, transportation, agriculture, e-media etc. 
 
 Findings: Evaluation of recommender system with help of metrics such as Precision and Recall is presented. Comparison of experimental results is presented with help of MAE and RMSE. Recommendation system helps to discover relevant insights and can be one of the vital technologies in future IoT solutions.
 
 Research, Practical & social implications: The research makes significant contribution by providing survey of existing recommender systems along with challenges faced while designing effective and accurate recommender. Various similarity measures to find similar users or items are investigated with future pointer direction. Recommender system help in decision making process.
 
 Originality/value: The results and conclusion obtained in this research are helpful in development of novel Recommender systems which definitely assist users to overcome Information Overload issue. It helps user to save network load as well.

MATHEMATICAL MODEL OF DIGITAL SIGNATURE BASED ON ECDSA AND SCHMIDT-SAMOA CRYPTOSYSTEM

Digital Signature Technology is replacing paper-based work for customers and employees in various industries and e-commercial environment. Digital Signature provides cryptographic services like authentication, non-repudiation, and integrity for the digital data. With the development of internet, Digital Signature becomes increasingly important for security because of its integrity and authenticity. It is an electronic signature that can be used to authenticate the identity of the sender. Digital Signature does not provide confidentiality until an encryption algorithm is applied. In this study, a new model of Digital Signature is introduced using the Elliptic Curve Digital Signature Algorithm (ECDSA)with an encryption technique Schmidt Samoa Cryptosystem. This model provides double layer security with encryption as well as signing protocol. Proposed model provides features like confidentiality, non-repudiation, and authenticity.

Can digital construction policy promote enterprise green innovation? A quasinatural experiment based on broadband China

PurposeThe purpose of this study is to explore how digital construction policy (DCP) drives enterprise green innovation (EGI) from an information processing theory (IPT) perspective, including the mediating mechanisms of market information accessibility and operational risk, the moderating role of intellectual property protection (IPP) and product market competition (PMC) and the heterogeneous effects of ownership, Internet development and managerial ability.Design/methodology/approachBased on the matched panel data of A-share listed enterprises from 2011 to 2019 and the Broadband China policy as a quasinatural experiment, this study investigates the impact of DCP on EGI by constructing a multi-time point difference-indifferences (DID) model.FindingsDigital construction policies can significantly promote EGI. DCP works in two fundamental ways, namely by increasing market information accessibility and reducing operational risk. IPP and PMC significantly increased the contribution of digital construction policies to EGI. Heterogeneity analysis found that digital technology has a stronger promotion effect for SOEs, high-managerial-ability enterprises and enterprises in regions with low Internet development levels.Practical implicationsThe study provides new insights about the antecedents of EGI from a DCP perspective. It also enlightens emerging economies to actualize green innovation under the digital wave.Originality/valueFrom the perspective of IPT, this study explains the mechanism of DCP-driven EGI. It enhances understanding of the relationship between DCP and EGI.

Research on the Teaching of Computer English under the Network Environment

The rapid development of Internet brings great challenges to computer English teaching in classroom. This paper summarizes the existing problems in the teaching of Computer English, analyzes the theoretical basis and advantages of the network environment, and solves two major problems in the teaching of Computer English under the network environment. One is the creation of English learning situations and model design, and the construction of relevant information resources to meet students' individual needs; The other is taking students as the center, cultivating their information processing technology and ability, and fully implementing the curriculum objectives. Through the analysis, it is found that under the network environment, the selection of teaching resources, the establishment and creation of the course environment, the classroom teaching methods and modes and the learning methods must follow the basic principles of complementarity and collaboration.

The Effect of Mobile Learning on Excretion System Materials on Cognitive Load and Student Concept Mastery

The fast development of internet and mobile technologies prompt this study. Mobile learning technology is accessible and convenient, as students can use advantageous devices to acquire knowledge at any time and location. Mobile learning can effectively meet students' educational requirements in acquiring knowledge. Furthermore, the design of learning materials that are inappropriate for mobile learning might be attributed to one of the reasons of students' cognitive stress. The purpose of this study is to see how mobile learning (Edmodo) on excretory system content affects cognitive load and student concept mastering. This research used a quasi-experimental method consisting of two classes: the experimental class (using mobile learning) and the control class (conventional learning). The instrument used to measure cognitive load consisted of questionnaires of intrinsic cognitive load, extraneous cognitive, and germane cognitive load, while the instrument for mastery of concepts consisted of pre-test and post-test. In addition, there was also a student response questionnaire to review the use of mobile learning in the experimental class. The data that had been collected were analyzed by a descriptive statistic method using SPSS 25.0. The results show that students in both classes have a cognitive load that can be controlled, but the cognitive load in the experimental class was more controllable than in the control class. The result of mastery of concept showed that the experimental class was higher than the control class, with a post-test significance value of 0.001 <0.05. Thus, it can be concluded that mobile learning influence the excretory system material on the cognitive load and mastery of students' concepts.

Legal Boundaries of Data Protection

With the development of Internet, big data and artificial intelligence technology, personal information protection has become the focus of social attention. The legal boundary of data protection has become increasingly prominent, involving the collection, storage, use, transmission and disclosure of personal information. This paper aims to explore the legal boundary of data protection, analyze the legal basis, legal principles and legal provisions of personal information protection, and put forward corresponding legal suggestions, in order to provide reference for the improvement of China's data protection legal system.

Current Situation and Future of Energy Internet Development

Current Situation and Future of Energy Internet Development

A Dynamic Task Scheduling Algorithm for Airborne Device Clouds

The rapid development of mobile Internet has promoted the rapid rise of cloud computing technology. Mobile terminal devices have greatly expanded the service capacity of mobile terminals by migrating complex computing tasks to run in the cloud. However, in the process of data exchange between mobile terminals and cloud computing centers, on the one hand, it consumes the limited power of mobile terminals, and on the other hand, it results in longer communication time, which negatively affects user QoE. Mobile cloud can effectively improve user QoE by shortening the data transmission distance, reducing the power consumption, and shortening the communication time at the same time. In this paper, we utilize the property that genetic algorithm can perform global search seeking the global optimal solution and construct a dynamic task scheduling model by combining the device-cloud link. The task scheduling model based on genetic algorithm and random scheduling algorithm is compared through comparison experiments, which show that the assignment time of the task scheduling model based on genetic algorithm is shortened by 11.82% to 48.51% and the energy consumption is reduced by 22.28% to 47.52% under different load conditions.

Unsupervised feature selection using chronological fitting with Shapley Additive explanation (SHAP) for industrial time-series anomaly detection

With the development of the industrial Internet of Things (IIOT), an amount of industrial multivariate time series (IMTS) data has been collected by various sensors. IMTS data anomaly detection plays an important role in industrial process monitoring and operation condition identification. Many real-world IMTS datasets usually have a large number of redundant features, which may lead to deviation of the anomaly detection model and misunderstanding of potential feature correlation. Hence, it is essential to select features that have critical and effective effects on anomalies and operation conditions. In this paper, a feature selection that introduces knowledge-based target variables into inner feature selectors (IFSs) is proposed as a wrapper method, referred to as ILSFS. The IFS is constructed by integrating a timing fitter, which is established based on the long and short-term memory (LSTM) network, with the Shapley Additive explanation (SHAP) assisted to eliminate unimportant features. The experiments were conducted on two datasets: the vertical roller mill dataset (VMD) of a slag milling factory in Jiangsu Province and the public server machine dataset (SMD). Compared with None feature selection, SVM-RFE, LGBM -Boruta, Genetic-based methods, and other SHAP-assisted feature selection methods such as XGBSVFIR and ShapHT+, the experiment results highlighted the proposed methodology ILSFS improves the best F1 score of each the above baselines by 0.2799, 0.2208, 0.2647, 0.1882, 0.3775, 0.2765 for the VMD and 0.1475, 0.0586, 0.0567, 0.0638, 0.0519, 0.2250 for the SMD on the downstream anomaly detection. The best precision of the ILSFS for the VMD and the SMD was 0.8224 and 0.9499, respectively, while its best recall was 0.7353 and 0.4902, respectively.

Privacy-preserving and verifiable data aggregation for Internet of Vehicles

With the development of Internet of Vehicles (IoV), organizations or institutions are increasingly interested in aggregating vehicle perception data to perform data analysis. Nevertheless, the existing solutions are difficult to meet the comprehensive requirements of IoV scenario, such as low latency, privacy preservation and rich functions. Additionally, it is more challenging that the aggregation node may return tampered or forged aggregation results for vulnerabilities or ulterior motives. To this end, we propose a Privacy-Preserving and Verifiable Data Aggregation Scheme for IoV (PPVDA), which allows the efficient aggregation for inner product over multi-dimensional data, and the data requester can verify the correctness and integrity of the aggregation results. By exploiting homomorphic MAC and secret sharing techniques, we construct a lightweight and verifiable mechanism for results as well as data privacy protection. We also conduct a comprehensive security analysis of PPVDA under the malicious security model. Moreover, extensive theoretical analyses and experimental evaluations demonstrate that PPVDA performs efficiently while retaining more desired properties. When dealing with 4×104 VNs, 3000 RSUs and 500-dimensional vectors, each EN side only takes about 74.5 ms and the DR side only takes about 3.8 ms, is therefore suitable for practical IoV scenarios.