Big Data Transfer Research Articles

Enhanced Network Intrusion Detection System for Internet of Things Security Using Multimodal Big Data Representation with Transfer Learning and Game Theory.

Internet of Things (IoT) applications and resources are highly vulnerable to flood attacks, including Distributed Denial of Service (DDoS) attacks. These attacks overwhelm the targeted device with numerous network packets, making its resources inaccessible to authorized users. Such attacks may comprise attack references, attack types, sub-categories, host information, malicious scripts, etc. These details assist security professionals in identifying weaknesses, tailoring defense measures, and responding rapidly to possible threats, thereby improving the overall security posture of IoT devices. Developing an intelligent Intrusion Detection System (IDS) is highly complex due to its numerous network features. This study presents an improved IDS for IoT security that employs multimodal big data representation and transfer learning. First, the Packet Capture (PCAP) files are crawled to retrieve the necessary attacks and bytes. Second, Spark-based big data optimization algorithms handle huge volumes of data. Second, a transfer learning approach such as word2vec retrieves semantically-based observed features. Third, an algorithm is developed to convert network bytes into images, and texture features are extracted by configuring an attention-based Residual Network (ResNet). Finally, the trained text and texture features are combined and used as multimodal features to classify various attacks. The proposed method is thoroughly evaluated on three widely used IoT-based datasets: CIC-IoT 2022, CIC-IoT 2023, and Edge-IIoT. The proposed method achieves excellent classification performance, with an accuracy of 98.2%. In addition, we present a game theory-based process to validate the proposed approach formally.

A new era of ICTs for combating symptoms of neurodevelopmental disorders

Information Communication Technology (ICT) is an umbrella definition, where the purpose of its creation and use is to facilitate communication, storage, and transfer of big data and the development and use of the technology that supports it. In contemporary research, several studies have observed attempts to incorporate them in populations with neurodevelopmental disorders (NDDs) to detect/assess, intervene, and improve deficits for the inclusion of these individuals in education and society. This review attempts to explore new universal and mainstream technology and to broaden and provide data for employing it in future research or intervention in the school setting as a complementary addition to education combating core symptoms of the three most recurring NDDs including autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD) and developmental coordination disorder (DCD).

Reliable Gamma-Interconnection Network for Data Analysis in Sensor Networks: Design and Performance Evaluation

In today’s era of high speed 5G internet all electronic sensor networks are connected through IoT. Bank transactions are digitized, people can access any data through their mobile phones, organizations and companies handle their projects through online meetings etc. Military and medical surveillance, navy navigation, weapon controlling, weather forecasting etc. involve big data analysis collected from sensors, that too at a very high speed with reliable results. This requires large number of parallel processors connected with huge Bank of memory modules to store big data. Reliable interconnection network is needed to connect these large number of parallel processors and memory modules efficiently hence Multistage Interconnection Networks (MINs) come into play, as they provide highly reliable communication for big data transfer between processors and memory modules whenever required. In this manuscript a new network named Reliable Gamma-interconnection Network (RGN) is introduced which possesses multiple paths between processors and memory modules with two totally disjoint path availability. It provides high reliability and minimum path distance between source node to destination node than other gamma networks known, with the minimum hardware complexity. Reliability estimation and evaluation of RGN has been presented in this paper and comparison of results achieved with other gamma networks has been done for validation purpose.

Distributed deep learning approach for intrusion detection system in industrial control systems based on big data technique and transfer learning

ABSTRACT Industry 4.0 refers to a new generation of connected and intelligent factories that is driven by the emergence of new technologies such as artificial intelligence, Cloud computing, Big Data and industrial control systems (ICS) in order to automate all phases of industrial operations. The presence of connected systems in industrial environments poses a considerable security challenge, moreover with the huge amount of data generated daily, there are complex attacks that occur in seconds and target production lines and their integrity. But, until now, factories do not have all the necessary tools to protect themselves, they mainly use traditional protection. In order to improve industrial control systems in terms of efficiency and response time, the present paper propose a new distributed intrusion detection approach using artificial intelligence methods, Big Data techniques and deployed in a cloud environment. A variety of Machine Learning and Deep Learning algorithms, basically convolutional neural networks (CNN), have been tested to compare performance and choose the most suitable model for the classification. We test the performance of our model by using the industrial dataset SWat.

CRBF: Cross-Referencing Bloom-Filter-Based Data Integrity Verification Framework for Object-Based Big Data Transfer Systems

Various components are involved in the end-to-end path of data transfer. Protecting data integrity from failures in these intermediate components is a key feature of big data transfer tools. Although most of these components provide some degree of data integrity, they are either too expensive or inefficient in recovering corrupted data. This problem highlights the need for application-level end-to-end integrity verification during data transfer. However, the computational, memory, and storage overhead of big data transfer tools can be a significant bottleneck for ensuring data integrity due to the large size of the data. This paper proposes a novel framework for data integrity verification in big data transfer systems using a cross-referencing Bloom filter. This framework has three advantages over state-of-the-art data integrity techniques: lower computation and memory overhead and zero false-positive errors for a limited number of elements. This study evaluates the computation, memory, recovery time, and false-positive overhead for the proposed framework and compares them with state-of-the-art solutions. The evaluation results indicate that the proposed framework is efficient in detecting and recovering from integrity errors while eliminating false positives in the Bloom filter data structure. In addition, we observe negligible computation, memory, and recovery overheads for all workloads.

METHODOLOGY FOR CLASSIFYING MONEY LAUNDERING RISKS TYPOLOGIES

The method of classifying the typologies of money laundering and terrorist financing risks in big data from a variety of data sources of organizational systems is investigated. The main provisions of this technique are theoretically substantiated, reference models and calculation formulas are given. An algebraic approach for the synthesis of the method is proposed, a theoretical justification of the feasibility of its use is given. The algebraic approach to the synthesis of the methodology is implemented on the basis of category theory and the theory of algorithms, in particular the theory of Markov algorithms, which is considered outside of normal algorithms. The N-scheme of the Markov algorithm is considered, which was developed to replace its well-known g-scheme and provides processing of words in natural, artificial and combined languages, which can be represented in an ordered and extended Markov alphabet A±2, which takes into account many sources of big data. Methods for inputting incomplete, heterogeneous (heterogeneous) and non-representative input data samples are given. A method is proposed for converting input data samples into cycles of the Big Data Transfer Protocol. A reference model of the legal economic activity of the organizational system has been synthesized. This reference model is subjected to analysis and signs of illegal economic activity of mutually interacting organizational systems are highlighted. A reference model of the system for classifying the risks of laundering proceeds from crime and financing of terrorism has been developed. To combat money laundering, the N-scheme of the Markov algorithm for one data source and the Markov system, consisting of such schemes, for a variety of big data sources are theoretically substantiated. It is concluded that the synthesized Markov system provides a decomposition of words and their morphisms, contains a common data input control channel, and provides a classification of money laundering risks.

Challenges of Emerging Wearable Sensors for Remote Monitoring toward Telemedicine Healthcare.

Digitized telemedicine tools with the Internet of Things (IoT) started advancing into our daily lives and have been incorporated with commercial wearable gadgets for noninvasive remote health monitoring. The newly established tools have been steered toward a new era of decentralized healthcare. The advancement of a telemedicine wearable monitoring system has attracted enormous interest in the multimodal big data acquisition of real-time physiological and biochemical information via noninvasive methods for any health-related industries. The expectation of telemedicine wearable creation has been focused on early diagnosis of multiple diseases and minimizing the cost of high-tech and invasive treatments. However, only limited progress has been directed toward the development of telemedicine wearable sensors. This Perspective addresses the advancement of these wearable sensors that encounter multiple challenges on the forefront and technological gaps hampering the realization of health monitoring at molecular levels related to smart materials mostly limited to single use, issues of selectivity to analytes, low sensitivity to targets, miniaturization, and lack of artificial intelligence to perform multiple tasks and secure big data transfer. Sensor stability with minimized signal drift, on-body sensor reusability, and long-term continuous health monitoring provides key analytical challenges. This Perspective also focuses on, promotes, and highlights wearable sensors with a distinct capability to interconnect with telemedicine healthcare for physical sensing and multiplex sensing at deeper levels. Moreover, it points out some critical challenges in different material aspects and promotes what it will take to advance the current state-of-art wearable sensors for telemedicine healthcare. Ultimately, this Perspective is to draw attention to some potential blind spots of wearable technology development and to inspire further development of this integrated technology in mitigating multimorbidity in aging societies through health monitoring at molecular levels to identify signs of diseases.

TPBF: Two-Phase Bloom-Filter-Based End-to-End Data Integrity Verification Framework for Object-Based Big Data Transfer Systems

Computational science simulations produce huge volumes of data for scientific research organizations. Often, this data is shared by data centers distributed geographically for storage and analysis. Data corruption in the end-to-end route of data transmission is one of the major challenges in distributing the data geographically. End-to-end integrity verification is therefore critical for transmitting such data across data centers effectively. Although several data integrity techniques currently exist, most have a significant negative influence on the data transmission rate as well as the storage overhead. Therefore, existing data integrity techniques are not viable solutions in high performance computing environments where it is very common to transfer huge volumes of data across data centers. In this study, we propose a two-phase Bloom-filter-based end-to-end data integrity verification framework for object-based big data transfer systems. The proposed solution effectively handles data integrity errors by reducing the memory and storage overhead and minimizing the impact on the overall data transmission rate. We investigated the memory, storage, and data transfer rate overheads of the proposed data integrity verification framework on the overall data transfer performance. The experimental findings showed that the suggested framework had 5% and 10% overhead on the total data transmission rate and on the total memory usage, respectively. However, we observed significant savings in terms of storage requirements, when compared with state-of-the-art solutions.

An SDN-Coordinated Steering Framework for Multipath Big Data Transfer Application

Data Transmission is a primary mechanism that can affect the performance of distributed storage systems. The traditional single-path transmission protocols are not efficient to serve several requirements of big data applications. In this paper, we propose an SDN-coordinated steering framework for multipath big data transfer applications. Multipath TCP protocol (MPTCP) coupled with SDN are mainly used for big data transfer in our framework. This framework is useful and cost-effective for OpenFlow networks and overlay networks. To provide a practical multipath transmission scheme for big data transfer applications using MPTCP, we propose a novel OpenFlow-Stats routing algorithm. In our algorithm, a new topology-pruning technique is applied, and the transmission paths are selected based on switch-port statistics. Our proposed framework is implemented and evaluated using the Mininet emulator and ONOS controller. The results show that our routing scheme can reduce the completion time of big data transfer up to 90% compared with the traditional routing with disjoint paths and up to 35% compared with the previous work. Moreover, our proposed routing is more scalable than other previous works in that it can provide lower complexity and system overhead. The results show that our routing scheme produces 57% less overhead compared with the previous work.

Abstract 9220: Employing a Convolutional Neural Network Facilitates Unsupervised Clustering of Aortic Outflow Velocity Profiles from Patients With Severe Aortic Stenosis

Introduction: The aortic outflow velocity profile is key to diagnose severe aortic stenosis (AS), and its shape reflects both aortic valve obstruction and left ventricular contractility. Hypothesis: Hypothesizing that aortic outflow velocity profiles contain more valuable information than can be captured by the human eye, features of the complex geometry of Doppler tracings were extracted by a convolutional neural network (CNN) pre-trained on big data (transfer learning). Methods: After pre-training a CNN (VGG-16) on a large data set (ImageNet data set; 14 million images belonging to 1,000 classes), the convolutional part was employed to transform Doppler tracings to 1D arrays. Principal component analysis (PCA) and k-means clustering of those 1D arrays were performed, and association between cluster and 2-year all-cause mortality was hereinafter assessed. Among 366 eligible patients with pre-procedural echocardiography and right heart catheterization prior to transcatheter aortic valve replacement (TAVR) for severe AS, good quality Doppler tracings from 101 patients were analyzed. Results: Among 101 patients (mean age: 79.3 ± 6.78 years; 49 [48.5%] women), estimated 2-year survival was 83.0% (95% CI: 75.1-91.7%). The convolutional part of the pre-trained VGG-16 model in conjunction with PCA and k-means clustering distinguished 2 shapes of aortic outflow velocity profiles. Kaplan-Meier analysis revealed that mortality in patients from cluster 2 (n = 40 [39.6%]) was significantly increased (HR for 2-year mortality: 3 [95% CI: 1-8.9]). Apart from reduced cardiac output and mean aortic valve gradient (AVG mean ), patients from cluster 2 were also characterized by signs of pulmonary hypertension, impaired right ventricular function and right atrial enlargement. Conventional dichotomization according to AVG mean confirmed that patients with AVG mean < 40 mmHg (n = 54 [53.5%]) decease earlier, but no statistical significance was reached (HR for 2-year mortality: 1.8 [95% CI: 0.59-5.2]). Conclusions: This is a proof-of-principle that transfer learning aids in agnostic interpretation of aortic outflow velocity profiles, thereby holding the promise to refine prognostic resolution for survival after TAVR.

Deep Neural Network Pretrained by Weighted Autoencoders and Transfer Learning for Retention Time Prediction of Small Molecules.

Retention time (RT) prediction contributes to identification of small molecules measured by high-performance liquid chromatography coupled with high-resolution mass spectrometry. Deep learning algorithms based on big data can enhance the accuracy of RT prediction. But at different chromatographic conditions, RTs of compounds are different, and the number of compounds with known RTs is small in most cases. Therefore, the transfer of big data is necessary. In this work, a strategy using a deep neural network (DNN) pretrained by weighed autoencoders and transfer learning (DNNpwa-TL) was proposed to efficiently predict RTs of compounds. The loss function in the autoencoders was calculated with features weighted by mutual information. Then, a DNN pretrained by weighted autoencoders (DNNpwa) was produced. For other specific chromatographic methods, the transfer learning model DNNpwa-TLs were built through fine-tuning the DNNpwa with the help of some compounds with known RTs to conduct the RT prediction. With the above strategy, a DNNpwa was first built with the METLIN small molecule retention time data set containing 80 038 small molecule compounds. A median relative error of 3.1% and a mean relative error of 4.9% were achieved. Then, 17 data sets from different chromatographic methods were studied, and the results showed that the performance of DNNpwa-TL was better than those of other deep learning models. Besides, DNNpwa-TL outperformed random forest, gradient boost, least absolute shrinkage and selection operator regression, and DNN for most of the 17 data sets. Therefore, DNNpwa-TL can provide an efficient method to perform RT prediction of small molecule compounds for different chromatographic methods and conditions.

Improving performance and data transmission security in VANETs

This article proposes a new approach to achieve fast and reliable transfer of data and uses machine learning techniques for data processing to improve the performance and data transmission security of the vehicular network. The proposed approach is the combination of 5G cellular network and alternative data transmission channels. The data collection experiment took place within different areas of the city of Berlin over a 3-month time period and involved the use of 5G technologies. The study carried out the analysis and classification of big data with the help of position-based routing protocols and the Support Vector Machine algorithms. The said techniques were employed to detect non-line-of-sight (NLoS) conditions in real time, which ensure the secure transmission of data without the loss or degradation of network performance. The novelty of the work is that it tackles various traffic scenarios (the extent of road congestion can affect the quality of big data transmission) and offers a way to improve big data transfer using the Support Vector Machine technology. The study results show that the proposed approach is effective enough with big data and can be employed to improve the performance of urban VANET networks and data transmission security. The study results can be useful in developing high-performance 5G-VANET applications to improve traffic safety in urban vehicular environments.

Thermal state of a conical antenna cooled by means of nanofluid saturated porous media

Thermal state of a conical antenna used for big data transfer was determined in this work. Its cooling is provided through porous media saturated with water-based copper nanofluid (NF) whose volume fraction varies in the 0% (pure water) [Formula: see text] range. Otherwise, the ratio between the thermal conductivity of the highly porous material and that of the fluid base (water) varies between 4 and 41.2. The solution is obtained by means of 3D numerical approach based on the volume control method using the SIMPLE algorithm in the large [Formula: see text]–[Formula: see text] Rayleigh number range. The average temperature of the antenna can be determined with the correlation proposed in this work for any combination of the thermal conductivity ratio, volume fraction and Rayleigh number. This new and original correlation makes it possible to determine the optimal values of these three influencing parameters to ensure the correct antenna’s operation.

On‐Line Fault Diagnosis Model of Distribution Transformer Based on Parallel Big Data Stream and Transfer Learning

Aiming at the problem of calculation speed for distribution transformer on‐line fault diagnosis, and the shortage of on‐line monitoring type and routine test data, an on‐line fault diagnosis model of distribution transformer based on parallel big data stream and transfer learning is established. First, the fault identification feasibility based on the present on‐line monitoring data of distribution transformer is analyzed, and the main indicators of on‐line fault identification are proposed. Second, the on‐line fault identification method based on ARIMA is proposed, on this basis, the on‐line fault identification model of distribution transformer based on big data stream is established, and the distribution transformers that possess hidden fault are selected, which would be diagnosed further. In order to improve the efficiency, the model is completed on Storm platform in parallel form. Then, the distribution transformer fault diagnosis indicator system is construct. In order to achieve fault diagnosis of the above selected distribution transformers, the effective fault information from other distribution transformers is extracted using the transfer learning algorithm TrAdaBoost, which is used as auxiliary data for the fault diagnosis tool training of the distribution transformer to be diagnosed. At the same time, the model is completed on Storm platform to improve the efficiency. Finally, based on the distribution transformer fault data, the distribution transformer fault diagnosis is simulated, and results show that the fault diagnosis is accurate and efficient. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Presentation of a new method based on modern multivariate approaches for big data replication in distributed environments.

As the amounts of data and use of distributed systems for data storage and processing have increased, reducing the number of replications has turned into a crucial requirement in these systems, which has been addressed by plenty of research. In this paper, an algorithm has been proposed to reduce the number of replications in big data transfer and, eventually to lower the traffic load over the grid by classifying data efficiently and optimally based on the sent data types and using VIKOR as a method of multivariate decision-making for ranking replication sites. Considering different variables, the VIKOR method makes it possible to take all the parameters effective in the assessment of site ranks into account. According to the results and evaluations, the proposed method has exhibited an improvement by about thirty percent in average over the LRU, LFU, BHR, and Without Rep. algorithms. Furthermore, it has improved the existing multivariate methods through different approaches to replication by thirty percent, as it considers effective parameters such as time, the number of replications, and replication site, causing replication to occur when it can make an improvement in terms of access.

Determining the best location for indoor access point based on received signal strength

Recently, a massive demand for wireless communication for big data transfer has risen. However, the interface and high noise stem from a large number of wireless local area networks require a better improvement of our own wireless devices; one of the best solutions is to locate an optimal location for the access point to achieve good received signal strength. In this research, a case study of a large house is simulated using Wireless Insite software to find the optimum location of the access point to investigate better coverage area. In addition, the performance of wave propagation for frequencies of 2.4 GHz and 5 GHz are carried out to show the differences between them. The results help to find the optimum location that gives optimal coverage area. Also, the results show that using 5 GHz leads to significant attenuation in received power by about 12.744%; this means that the transmitted power of the 5 GHz has to be increased by the same percentage to achieve the same coverage area when using 2.4 GHz.

Exploratory analysis and performance prediction of big data transfer in High-performance Networks

Big data transfer in large-scale scientific and business applications is increasingly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) via advance bandwidth reservation. Provisioning agents need to carefully schedule data transfer requests, compute network paths, and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized, could be simply wasted due to the exclusive access during the approved time window, and cause extra overhead and complexity for resource management. This calls for accurate performance prediction to reserve bandwidths that match actual needs and avoid over-provisioning. We employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements collected in the past several years from data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated testbeds. We first analyze the performance patterns in response to a comprehensive list of parameters in end-host systems, network connections, and data transfer applications, which motivate the use of machine learning and also help us identify the effects of latent factors. We then propose threshold- and clustering-based methods to eliminate negative effects of latent factors in data preprocessing and build a robust performance predictor based on customized domain-oriented loss functions. The performance of the proposed methods is verified by extensive experiments using SVR and RFR as well as theoretical analysis of the general performance bound.

Data Transfers Using Bandwidth Reservation Through Multiple Disjoint Paths of Dynamic HPNs

Bandwidth reservation was initially designed to provide QoS for real-time multimedia applications, and has been extended to many other applications and widely used because of its proved high efficiency and effectiveness. Particularly, it has now become an indispensable service to support big data transfers in dynamic high-performance networks (HPNs) for remote data storage and processing. A number of problems regarding data transfers using bandwidth reservation have been investigated in the past few years, and most of them focused on the data transfers on one path or two disjoint paths of the dynamic HPNs. In this paper, three important problems regarding data transfers using bandwidth reservation on multiple node- and edge-disjoint paths in dynamic HPNs to achieve are studied: (i) the earliest completion time for a data transfer request through multiple disjoint variable paths with variable bandwidths, (ii) the earliest completion time for a data transfer request through multiple disjoint fixed paths with fixed bandwidths, and (iii) the minimum number of disjoint fixed paths with fixed bandwidths for a deadline-constrained data transfer request. We prove all of these problems to be NP-complete and propose one heuristic algorithm for each. We compare the proposed algorithms with two existing scheduling algorithms and one algorithm derived from an existing algorithm, and conduct extensive simulations. The simulation results show that our proposed heuristic algorithms have much better overall scheduling performance.

A Two-Phase Dynamic Throughput Optimization Model for Big Data Transfers

The amount of data transferred over dedicated and non-dedicated network links has been increasing much faster than the increase in the network capacity. On the other hand, the current data transfer solutions fail to guarantee even the promised achievable transfer throughput. In this article, we propose a novel two-phase dynamic throughput optimization model based on mathematical modeling with offline knowledge discovery/analysis and adaptive online decision making. In the offline analysis, we mine historical transfer logs to perform knowledge discovery about the transfer characteristics. The online phase uses the discovered knowledge from the offline analysis along with the real-time investigation of the network condition to optimize the protocol parameters. As the real-time investigation is expensive and provides partial knowledge about the current network status, our model uses historical knowledge about the network and data characteristics to reduce the real-time investigation overhead while ensuring near-optimal throughput for each transfer. Our novel approach is tested over different networks with different datasets, and it has outperformed its closest competitor by 1.7x and the default case by 5x. It also achieved up to 93 percent accuracy compared to the optimal achievable throughput possible on those networks.

SSII: Secured and High-Quality Steganography Using Intelligent Hybrid Optimization Algorithms for IoT

Internet of Things (IoT) is a domain where the transfer of big data is taking place every single second. The security of these data is a challenging task; however, security challenges can be mitigated with cryptography and steganography techniques. These techniques are crucial when dealing with user authentication and data privacy. In the proposed work, a highly secured technique is proposed using IoT protocol and steganography. This work proposes an image steganography procedure by utilizing the combination of various algorithms that build the security of the secret data by utilizing Binary bit-plane decomposition (BBPD) based image encryption technique. Thereafter a Salp Swarm Optimization Algorithm (SSOA) based adaptive embedding process is proposed to increase the payload capacity by setting different parameters in the steganographic embedding function for edge and smooth blocks. Here the SSOA algorithm is used to localize the edge and smooth blocks efficiently. Then, the hybrid Fuzzy Neural Network with a backpropagation learning algorithm is used to enhance the quality of the stego images. Then these stego images are transferred to the destination in the highly secured protocol of IoT. The proposed steganography technique shows better results in terms of security, image quality, and payload capacity in comparison with the existing state of art methods.