Future Internet

23 pages, 567 KiB

Open AccessArticle

Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks

by Gianfranco Lombardo, Mattia Pellegrino, George Adosoglou, Stefano Cagnoni, Panos M. Pardalos and Agostino Poggi

Future Internet 2022, 14(8), 244; https://doi.org/10.3390/fi14080244 - 22 Aug 2022

Cited by 26 | Viewed by 6356

Abstract

Predicting corporate bankruptcy is one of the fundamental tasks in credit risk assessment. In particular, since the 2007/2008 financial crisis, it has become a priority for most financial institutions, practitioners, and academics. The recent advancements in machine learning (ML) enabled the development of [...] Read more.

Predicting corporate bankruptcy is one of the fundamental tasks in credit risk assessment. In particular, since the 2007/2008 financial crisis, it has become a priority for most financial institutions, practitioners, and academics. The recent advancements in machine learning (ML) enabled the development of several models for bankruptcy prediction. The most challenging aspect of this task is dealing with the class imbalance due to the rarity of bankruptcy events in the real economy. Furthermore, a fair comparison in the literature is difficult to make because bankruptcy datasets are not publicly available and because studies often restrict their datasets to specific economic sectors and markets and/or time periods. In this work, we investigated the design and the application of different ML models to two different tasks related to default events: (a) estimating survival probabilities over time; (b) default prediction using time-series accounting data with different lengths. The entire dataset used for the experiments has been made available to the scientific community for further research and benchmarking purposes. The dataset pertains to 8262 different public companies listed on the American stock market between 1999 and 2018. Finally, in light of the results obtained, we critically discuss the most interesting metrics as proposed benchmarks for future studies. Full article

(This article belongs to the Special Issue Machine Learning Perspective in the Convolutional Neural Network Era)

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20 pages, 997 KiB

Open AccessArticle

A Blockchain-Based Framework to Enhance Anonymous Services with Accountability Guarantees

by Francesco Buccafurri, Vincenzo De Angelis and Sara Lazzaro

Future Internet 2022, 14(8), 243; https://doi.org/10.3390/fi14080243 - 21 Aug 2022

Cited by 10 | Viewed by 2424

Abstract

Anonymous service delivery has attracted the interest of research and the industry for many decades. To obtain effective solutions, anonymity should be guaranteed against the service provider itself. However, if the full anonymity of users is implemented, no accountability mechanism can be provided. [...] Read more.

Anonymous service delivery has attracted the interest of research and the industry for many decades. To obtain effective solutions, anonymity should be guaranteed against the service provider itself. However, if the full anonymity of users is implemented, no accountability mechanism can be provided. This represents a problem, especially when referring to scenarios in which a user, protected by anonymity, may perform illegally when leveraging the anonymous service. In this paper, we propose a blockchain-based solution to the trade-off between anonymity and accountability. In particular, our solution relies on three independent parties (one of which is the service provider itself) such that only the collaboration of all three actors allows for the disclosure of the real identity of the user. In all other cases, anonymity is guaranteed. To show the feasibility of the proposal, we developed a prototype with user-friendly interfaces that minimize the client-side operations. Our solution is then also effective from the point of view of usability. Full article

(This article belongs to the Special Issue State-of-the-Art Future Internet Technology in Italy 2022–2023)

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23 pages, 494 KiB

Open AccessArticle

On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information

by Georgios Kallitsis, Vasileios Karyotis and Symeon Papavassiliou

Future Internet 2022, 14(8), 242; https://doi.org/10.3390/fi14080242 - 17 Aug 2022

Cited by 2 | Viewed by 1716

Abstract

In this paper, we study the potential of using the metric of Age of Information (AoI) for enhancing delay-tolerant routing protocols. The latter have been proposed for alleviating the impact of long roundtrip time in networks operating in harsh environments, e.g., in distributed [...] Read more.

In this paper, we study the potential of using the metric of Age of Information (AoI) for enhancing delay-tolerant routing protocols. The latter have been proposed for alleviating the impact of long roundtrip time in networks operating in harsh environments, e.g., in distributed applications deployed in a desert/sparsely populated area without infrastructure, a space network, etc. Delay-tolerant routing protocols can prevent excessive packet timer expiration, but do not provide any packet delivery time guarantee. Thus, they are unsuitable for time-sensitive applications that are more intensely desired nowadays in the next generation networking applications. By incorporating AoI into the operation of delay-tolerant routing protocols, we aim at devising routing protocols that can cope with both long propagation times and challenges related to time-sensitivity in packet delivery. More specifically, in this work, we modify the operation of a well-known delay-tolerant routing protocol, namely FRESH, to make AoI-based packet forwarding decisions, aiming at achieving specific delay guarantees regarding the end-to-end delivery time. We investigate the advantages and disadvantages of such an approach compared to the traditional FRESH protocol. This work serves as a cornerstone for successfully demonstrating the potential of exploiting AoI in delay-tolerant routing and its applications. Full article

(This article belongs to the Special Issue Secure Communication Protocols for Future Computing)

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17 pages, 1472 KiB

Open AccessArticle

A Novel Logo Identification Technique for Logo-Based Phishing Detection in Cyber-Physical Systems

by Padmalochan Panda, Alekha Kumar Mishra and Deepak Puthal

Future Internet 2022, 14(8), 241; https://doi.org/10.3390/fi14080241 - 15 Aug 2022

Cited by 6 | Viewed by 3013

Abstract

The first and foremost task of a phishing-detection mechanism is to confirm the appearance of a suspicious page that is similar to a genuine site. Once this is found, a suitable URL analysis mechanism may lead to conclusions about the genuineness of the [...] Read more.

The first and foremost task of a phishing-detection mechanism is to confirm the appearance of a suspicious page that is similar to a genuine site. Once this is found, a suitable URL analysis mechanism may lead to conclusions about the genuineness of the suspicious page. To confirm appearance similarity, most of the approaches inspect the image elements of the genuine site, such as the logo, theme, font color and style. In this paper, we propose a novel logo-based phishing-detection mechanism that characterizes the existence and unique distribution of hue values in a logo image as the foundation to unambiguously represent a brand logo. Using the proposed novel feature, the detection mechanism optimally classifies a suspicious logo to the best matching brand logo. The experiment is performed over our customized dataset based on the popular phishing brands in the South-Asia region. A set of five machine-learning algorithms is used to train and test the prepared dataset. We inferred from the experimental results that the ensemble random forest algorithm achieved the high accuracy of 87% with our prepared dataset. Full article

(This article belongs to the Special Issue Security and Community Detection in Social Network)

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18 pages, 4855 KiB

Open AccessArticle

Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder

by Ahmed Latif Yaser, Hamdy M. Mousa and Mahmoud Hussein

Future Internet 2022, 14(8), 240; https://doi.org/10.3390/fi14080240 - 12 Aug 2022

Cited by 22 | Viewed by 3615

Abstract

Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to [...] Read more.

Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybrid technique to recognize denial-of-service (DDoS) attacks that combine deep learning and feedforward neural networks as autoencoders. Two datasets were analyzed for the training and testing model, first statically and then iteratively. The auto-encoding model is constructed by stacking the input layer and hidden layer of self-encoding models’ layer by layer, with each self-encoding model using a hidden layer. To evaluate our model, we use a three-part data split (train, test, and validate) rather than the common two-part split (train and test). The resulting proposed model achieved a higher accuracy for the static dataset, where for ISCX-IDS-2012 dataset, accuracy reached a high of 99.35% in training, 99.3% in validation and 99.99% in precision, recall, and F1-score. for the UNSW2018 dataset, the accuracy reached a high of 99.95% in training, 0.99.94% in validation, and 99.99% in precision, recall, and F1-score. In addition, the model achieved great results with a dynamic dataset (using an emulator), reaching a high of 97.68% in accuracy. Full article

(This article belongs to the Section Cybersecurity)

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27 pages, 4416 KiB

Open AccessArticle

Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks

by Usman Ali, Giuseppe Caso, Luca De Nardis, Konstantinos Kousias, Mohammad Rajiullah, Özgü Alay, Marco Neri, Anna Brunstrom and Maria-Gabriella Di Benedetto

Future Internet 2022, 14(8), 239; https://doi.org/10.3390/fi14080239 - 11 Aug 2022

Cited by 8 | Viewed by 3791

Abstract

The successful rollout of fifth-generation (5G) networks requires a full understanding of the behavior of the propagation channel, taking into account the signal formats and the frequencies standardized by the Third Generation Partnership Project (3GPP). In the past, channel characterization for 5G has [...] Read more.

The successful rollout of fifth-generation (5G) networks requires a full understanding of the behavior of the propagation channel, taking into account the signal formats and the frequencies standardized by the Third Generation Partnership Project (3GPP). In the past, channel characterization for 5G has been addressed mainly based on the measurements performed on dedicated links in experimental setups. This paper presents a state-of-the-art contribution to the characterization of the outdoor-to-indoor radio channel in the 3.5 GHz band, based on experimental data for commercial, deployed 5G networks, collected during a large scale measurement campaign carried out in the city of Rome, Italy. The analysis presented in this work focuses on downlink, outdoor-to-indoor propagation for two operators adopting two different beamforming strategies, single wide-beam and multiple synchronization signal blocks (SSB) based beamforming; it is indeed the first contribution studying the impact of beamforming strategy in real 5G networks. The time and power-related channel characteristics, i.e., mean excess delay and Root Mean Square (RMS) delay spread, path loss, and K-factor are studied for the two operators in multiple measurement locations. The analysis of time and power-related parameters is supported and extended by a correlation analysis between each pair of parameters. The results show that beamforming strategy has a marked impact on propagation. A single wide-beam transmission leads, in fact, to lower RMS delay spread and lower mean excess delay compared to a multiple SSB-based transmission strategy. In addition, the single wide-beam transmission system is characterized by a smaller path loss and a higher K-factor, suggesting that the adoption of a multiple SSB-based transmission strategy may have a negative impact on downlink performance. Full article

(This article belongs to the Special Issue Internet of Things and Cyber-Physical Systems)

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20 pages, 7194 KiB

Open AccessArticle

Will Zero Vulnerability Computing (ZVC) Ever Be Possible? Testing the Hypothesis

by Fazal Raheman, Tejas Bhagat, Brecht Vermeulen and Peter Van Daele

Future Internet 2022, 14(8), 238; https://doi.org/10.3390/fi14080238 - 30 Jul 2022

Cited by 2 | Viewed by 3587

Abstract

Life without computers is unimaginable. However, computers remain vulnerable to cybercrimes, a USD 6 trillion industry that the world has come to accept as a “necessary evil”. Third-party permissions resulting in an attack surface (AS) and in-computer storage that computers mandate are key [...] Read more.

Life without computers is unimaginable. However, computers remain vulnerable to cybercrimes, a USD 6 trillion industry that the world has come to accept as a “necessary evil”. Third-party permissions resulting in an attack surface (AS) and in-computer storage that computers mandate are key design elements that hackers exploit, formerly by remote malware installation and later by stealing personal data using authentication faking techniques. In legacy computers, the AS cannot be completely eliminated, nor can a connected device retain data offline, rendering fool-proof cybersecurity impossible. Although the architects of legacy computers made perfectly reasonable engineering trade-offs for their world, our world is very different. Zero vulnerability computing (ZVC) challenges the impossible with in-computer offline storage (ICOS) and Supra OS (SOS), to deliver comprehensive protection against vulnerabilities. The feasibility of ZVC is demonstrated in a tiny permanently computer-mounted hardware wallet, providing the first evidence of the complete obliteration of the AS. Malware cannot infect the ZVC device on account of lacking an AS, nor can personal data be hacked as they mostly remain offline, except for sporadic processing. Further research should explore whether ZVC can fully secure computers in more complex real-world scenarios and open a new epoch in the evolution of computers and the Internet. Full article

(This article belongs to the Section Cybersecurity)

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14 pages, 2745 KiB

Open AccessArticle

Digital Qualitative and Quantitative Analysis of Arabic Textbooks

by Francesca Fallucchi, Bouchra Ghattas, Riem Spielhaus and Ernesto William De Luca

Future Internet 2022, 14(8), 237; https://doi.org/10.3390/fi14080237 - 29 Jul 2022

Cited by 4 | Viewed by 2747

Abstract

Digital Humanities (DH) provide a broad spectrum of functionalities and tools that enable the enrichment of both quantitative and qualitative research methods in the humanities. It has been widely recognized that DH can help in curating and analysing large amounts of data. However, [...] Read more.

Digital Humanities (DH) provide a broad spectrum of functionalities and tools that enable the enrichment of both quantitative and qualitative research methods in the humanities. It has been widely recognized that DH can help in curating and analysing large amounts of data. However, digital tools can also support research processes in the humanities that are interested in detailed analyses of how empirical sources are patterned. Following a methodological differentiation between close and distant reading with regard to textual analysis, this article describes the Edumeres Toolbox, a digital tool for textbook analysis. The Edumeres Toolbox is an outcome of the continuous interdisciplinary exchange between computer scientists and humanist researchers, whose expertise is crucial to convert information into knowledge by means of (critical) interpretation and contextualization. This paper presents a use case in order to describe the various functionalities of the Edumeres Toolbox and their use for the analysis of a collection of Arabic textbooks. Hereby, it shows how the interaction between humanist researchers and computer scientists in this digital process produces innovative research solutions and how the tool enables users to discover structural and linguistic patterns and develop innovative research questions. Finally, the paper describes challenges recognized by humanist researchers in using digital tools in their work, which still require in-depth research and practical efforts from both parties to improve the tool performance. Full article

(This article belongs to the Special Issue Digital Analysis in Digital Humanities)

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21 pages, 7028 KiB

Open AccessArticle

Seeing through Wavy Water–Air Interface: A Restoration Model for Instantaneous Images Distorted by Surface Waves

by Bijian Jian, Chunbo Ma, Dejian Zhu, Yixiao Sun and Jun Ao

Future Internet 2022, 14(8), 236; https://doi.org/10.3390/fi14080236 - 29 Jul 2022

Cited by 3 | Viewed by 2484

Abstract

Imaging through a wavy water–air interface is challenging since light rays are bent by unknown amounts, leading to complex geometric distortions. Considering the restoration of instantaneous distorted images, this paper proposes an image recovery model via structured light projection. The algorithm is composed [...] Read more.

Imaging through a wavy water–air interface is challenging since light rays are bent by unknown amounts, leading to complex geometric distortions. Considering the restoration of instantaneous distorted images, this paper proposes an image recovery model via structured light projection. The algorithm is composed of two separate parts. In the first part, an algorithm for the determination of the instantaneous shape of the water surface via structured light projection is developed. Then, we synchronously recover the distorted airborne scene image through reverse ray tracing in the second part. The experimental results show that, compared with the state-of-the-art methods, the proposed method not only can overcome the influence of changes in natural illumination conditions for WAI reconstruction, but also can significantly reduce the distortion and achieve better performance. Full article

(This article belongs to the Special Issue Developments of Computer Vision and Image Processing: Methodologies and Applications)

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18 pages, 4899 KiB

Open AccessArticle

A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN

by Yaning Li, Hongsheng Li, Baoguo Yu and Jun Li

Future Internet 2022, 14(8), 235; https://doi.org/10.3390/fi14080235 - 29 Jul 2022

Cited by 1 | Viewed by 1771

Abstract

At present, the interaction mechanism between the complex indoor environment and pseudolite signals has not been fundamentally resolved, and the stability, continuity, and accuracy of indoor positioning are still technical bottlenecks. In view of the shortcomings of the existing indoor fingerprint positioning methods, [...] Read more.

At present, the interaction mechanism between the complex indoor environment and pseudolite signals has not been fundamentally resolved, and the stability, continuity, and accuracy of indoor positioning are still technical bottlenecks. In view of the shortcomings of the existing indoor fingerprint positioning methods, this paper proposes a hybrid CSI fingerprint method for indoor pseudolite positioning based on Ray Tracing and artificial neural network (RT-ANN), which combines the advantages of actual acquisition, deterministic simulation, and artificial neural network, and adds the simulation CSI feature parameters generated by modeling and simulation to the input of the neural network, extending the sample features of the neural network input dataset. Taking an airport environment as an example, it is proved that the hybrid method can improve the positioning accuracy in the area where the fingerprints have been collected, the positioning error is reduced by 54.7% compared with the traditional fingerprint positioning method. It is also proved that preliminary positioning can be completed in the area without fingerprint collection. Full article

(This article belongs to the Special Issue Wireless Technology for Indoor Localization System)

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19 pages, 662 KiB

Open AccessArticle

Microblog Sentiment Analysis Based on Dynamic Character-Level and Word-Level Features and Multi-Head Self-Attention Pooling

by Shangyi Yan, Jingya Wang and Zhiqiang Song

Future Internet 2022, 14(8), 234; https://doi.org/10.3390/fi14080234 - 29 Jul 2022

Cited by 5 | Viewed by 1992

Abstract

To address the shortcomings of existing deep learning models and the characteristics of microblog speech, we propose the DCCMM model to improve the effectiveness of microblog sentiment analysis. The model employs WOBERT Plus and ALBERT to dynamically encode character-level text and word-level text, [...] Read more.

To address the shortcomings of existing deep learning models and the characteristics of microblog speech, we propose the DCCMM model to improve the effectiveness of microblog sentiment analysis. The model employs WOBERT Plus and ALBERT to dynamically encode character-level text and word-level text, respectively. Then, a convolution operation is used to extract local key features, while cross-channel feature fusion and multi-head self-attention pooling operations are used to extract global semantic information and filter out key data, before using the multi-granularity feature interaction fusion operation to effectively fuse character-level and word-level semantic information. Finally, the Softmax function is used to output the results. On the weibo_senti_100k dataset, the accuracy and F1 values of the DCCMM model improve by 0.84% and 1.01%, respectively, compared to the best-performing comparison model. On the SMP2020-EWECT dataset, the accuracy and F1 values of the DCCMM model improve by 1.22% and 1.80%, respectively, compared with the experimental results of the best-performing comparison model. The results showed that DCCMM outperforms existing advanced sentiment analysis models. Full article

(This article belongs to the Special Issue Affective Computing and Sentiment Analysis)

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23 pages, 4322 KiB

Open AccessArticle

Hybrid Sensing Platform for IoT-Based Precision Agriculture

by Hamid Bagha, Ali Yavari and Dimitrios Georgakopoulos

Future Internet 2022, 14(8), 233; https://doi.org/10.3390/fi14080233 - 28 Jul 2022

Cited by 12 | Viewed by 3450

Abstract

Precision agriculture (PA) is the field that deals with the fine-tuned management of crops to increase crop yield, augment profitability, and conserve the environment. Existing Internet of Things (IoT) solutions for PA are typically divided in terms of their use of either aerial [...] Read more.

Precision agriculture (PA) is the field that deals with the fine-tuned management of crops to increase crop yield, augment profitability, and conserve the environment. Existing Internet of Things (IoT) solutions for PA are typically divided in terms of their use of either aerial sensing using unmanned aerial vehicles (UAVs) or ground-based sensing approaches. Ground-based sensing provides high data accuracy, but it involves large grids of ground-based sensors with high operational costs and complexity. On the other hand, while the cost of aerial sensing is much lower than ground-based sensing alternatives, the data collected via aerial sensing are less accurate and cover a smaller period than ground-based sensing data. Despite the contrasting virtues and limitations of these two sensing approaches, there are currently no hybrid sensing IoT solutions that combine aerial and ground-based sensing to ensure high data accuracy at a low cost. In this paper, we propose a Hybrid Sensing Platform (HSP) for PA—an IoT platform that combines a small number of ground-based sensors with aerial sensors to improve aerial data accuracy and at the same time reduce ground-based sensing costs. Full article

(This article belongs to the Special Issue Big Data Analytics for the Industrial Internet of Things)

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18 pages, 2003 KiB

Open AccessArticle

Integrating Elliptic Curve Cryptography with the Modbus TCP SCADA Communication Protocol

by Despoina Chochtoula, Aristidis Ilias, Yannis C. Stamatiou and Christos Makris

Future Internet 2022, 14(8), 232; https://doi.org/10.3390/fi14080232 - 28 Jul 2022

Cited by 5 | Viewed by 2414

Abstract

SCADA systems monitor critical industrial, energy and other physical infrastructures in order to detect malfunctions, issue alerts and, in many cases, propose or even take remedial actions. However, due to their attachment to the Internet, SCADA systems are, today, vulnerable to attacks such [...] Read more.

SCADA systems monitor critical industrial, energy and other physical infrastructures in order to detect malfunctions, issue alerts and, in many cases, propose or even take remedial actions. However, due to their attachment to the Internet, SCADA systems are, today, vulnerable to attacks such as, among several others, interception of data traffic, malicious modifications of settings and control operations data, malicious modification of measurements and infrastructure data and Denial-of-Service attacks. Our research focuses on strengthening SCADA systems with cryptographic methods and protection mechanisms with emphasis on data and messaging encryption and device identification and authentication. The limited availability of computing power and memory in sensors and embedded devices deployed in SCADA systems make render cryptographic methods with higher resource requirements, such as the use of conventional public key cryptography such as RSA, unsuitable. We, thus, propose Elliptic Curve Cryptography as an alternative cryptographic mechanism, where smaller key sizes are required, with lower resource requirements for cryptographic operations. Accordingly, our approach integrates Modbus, a commonly used SCADA communication protocol, with Elliptic Curve Cryptography. We have, also, developed an experimental set-up in order to demonstrate the performance of our approach and draw conclusions regarding its effectiveness in real SCADA installations. Full article

(This article belongs to the Special Issue Big Data Analytics for the Industrial Internet of Things)

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18 pages, 2856 KiB

Open AccessArticle

Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET

by Siti-Aisyah Zali, Shahbe Mat-Desa, Zarina Che-Embi and Wan-Noorshahida Mohd-Isa

Future Internet 2022, 14(8), 231; https://doi.org/10.3390/fi14080231 - 28 Jul 2022

Cited by 5 | Viewed by 2390

Abstract

Shadows in drone images commonly appear in various shapes, sizes, and brightness levels, as the images capture a wide view of scenery under many conditions, such as varied flying height and weather. This property of drone images leads to a major problem when [...] Read more.

Shadows in drone images commonly appear in various shapes, sizes, and brightness levels, as the images capture a wide view of scenery under many conditions, such as varied flying height and weather. This property of drone images leads to a major problem when it comes to detecting shadow and causes the presence of noise in the predicted shadow mask. The purpose of this study is to improve shadow detection results by implementing post-processing methods related to automatic thresholding and binary mask refinement. The aim is to discuss how the selected automatic thresholding and two methods of binary mask refinement perform to increase the efficiency and accuracy of shadow detection. The selected automatic thresholding method is Otsu’s thresholding, and methods for binary mask refinement are morphological operation and dense CRF. The study shows that the proposed methods achieve an acceptable accuracy of 96.43%. Full article

(This article belongs to the Collection Computer Vision, Deep Learning and Machine Learning with Applications)

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23 pages, 985 KiB

Open AccessArticle

Multi-Agent-Based Traffic Prediction and Traffic Classification for Autonomic Network Management Systems for Future Networks

by Sisay Tadesse Arzo, Zeinab Akhavan, Mona Esmaeili, Michael Devetsikiotis and Fabrizio Granelli

Future Internet 2022, 14(8), 230; https://doi.org/10.3390/fi14080230 - 28 Jul 2022

Cited by 5 | Viewed by 3305

Abstract

Recently, a multi-agent based network automation architecture has been proposed. The architecture is named multi-agent based network automation of the network management system (MANA-NMS). The architectural framework introduced atomized network functions (ANFs). ANFs should be autonomous, atomic, and intelligent agents. Such agents should [...] Read more.

Recently, a multi-agent based network automation architecture has been proposed. The architecture is named multi-agent based network automation of the network management system (MANA-NMS). The architectural framework introduced atomized network functions (ANFs). ANFs should be autonomous, atomic, and intelligent agents. Such agents should be implemented as an independent decision element, using machine/deep learning (ML/DL) as an internal cognitive and reasoning part. Using these atomic and intelligent agents as a building block, a MANA-NMS can be composed using the appropriate functions. As a continuation toward implementation of the architecture MANA-NMS, this paper presents a network traffic prediction agent (NTPA) and a network traffic classification agent (NTCA) for a network traffic management system. First, an NTPA is designed and implemented using DL algorithms, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), multilayer perceptrons (MLPs), and convolutional neural network (CNN) algorithms as a reasoning and cognitive part of the agent. Similarly, an NTCA is designed using decision tree (DT), K-nearest neighbors (K-NN), support vector machine (SVM), and naive Bayes (NB) as a cognitive component in the agent design. We then measure the NTPA prediction accuracy, training latency, prediction latency, and computational resource consumption. The results indicate that the LSTM-based NTPA outperforms compared to GRU, MLP, and CNN-based NTPA in terms of prediction accuracy, and prediction latency. We also evaluate the accuracy of the classifier, training latency, classification latency, and computational resource consumption of NTCA using the ML models. The performance evaluation shows that the DT-based NTCA performs the best. Full article

(This article belongs to the Special Issue Self-Driving Networks (SelfDN) and Artificial Intelligence)

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18 pages, 1245 KiB

Open AccessArticle

CCrFS: Combine Correlation Features Selection for Detecting Phishing Websites Using Machine Learning

by Jimmy Moedjahedy, Arief Setyanto, Fawaz Khaled Alarfaj and Mohammed Alreshoodi

Future Internet 2022, 14(8), 229; https://doi.org/10.3390/fi14080229 - 27 Jul 2022

Cited by 19 | Viewed by 3681

Abstract

Internet users are continually exposed to phishing as cybercrime in the 21st century. The objective of phishing is to obtain sensitive information by deceiving a target and using the information for financial gain. The information may include a login detail, password, date of [...] Read more.

Internet users are continually exposed to phishing as cybercrime in the 21st century. The objective of phishing is to obtain sensitive information by deceiving a target and using the information for financial gain. The information may include a login detail, password, date of birth, credit card number, bank account number, and family-related information. To acquire these details, users will be directed to fill out the information on false websites based on information from emails, adverts, text messages, or website pop-ups. Examining the website’s URL address is one method for avoiding this type of deception. Identifying the features of a phishing website URL takes specialized knowledge and investigation. Machine learning is one method that uses existing data to teach machines to distinguish between legal and phishing website URLs. In this work, we proposed a method that combines correlation and recursive feature elimination to determine which URL characteristics are useful for identifying phishing websites by gradually decreasing the number of features while maintaining accuracy value. In this paper, we use two datasets that contain 48 and 87 features. The first scenario combines power predictive score correlation and recursive feature elimination; the second scenario is the maximal information coefficient correlation and recursive feature elimination. The third scenario combines spearman correlation and recursive feature elimination. All three scenarios from the combined findings of the proposed methodologies achieve a high level of accuracy even with the smallest feature subset. For dataset 1, the accuracy value for the 10 features result is 97.06%, and for dataset 2 the accuracy value is 95.88% for 10 features. Full article

(This article belongs to the Section Big Data and Augmented Intelligence)

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15 pages, 895 KiB

Open AccessArticle

Automatic Detection of Sensitive Data Using Transformer- Based Classifiers

by Michael Petrolini, Stefano Cagnoni and Monica Mordonini

Future Internet 2022, 14(8), 228; https://doi.org/10.3390/fi14080228 - 27 Jul 2022

Cited by 5 | Viewed by 4390

Abstract

The General Data Protection Regulation (GDPR) has allowed EU citizens and residents to have more control over their personal data, simplifying the regulatory environment affecting international business and unifying and homogenising privacy legislation within the EU. This regulation affects all companies that process [...] Read more.

The General Data Protection Regulation (GDPR) has allowed EU citizens and residents to have more control over their personal data, simplifying the regulatory environment affecting international business and unifying and homogenising privacy legislation within the EU. This regulation affects all companies that process data of European residents regardless of the place in which they are processed and their registered office, providing for a strict discipline of data protection. These companies must comply with the GDPR and be aware of the content of the data they manage; this is especially important if they are holding sensitive data, that is, any information regarding racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data relating to the sexual life or sexual orientation of the person, as well as data on physical and mental health. These classes of data are hardly structured, and most frequently they appear within a document such as an email message, a review or a post. It is extremely difficult to know if a company is in possession of sensitive data at the risk of not protecting them properly. The goal of the study described in this paper is to use Machine Learning, in particular the Transformer deep-learning model, to develop classifiers capable of detecting documents that are likely to include sensitive data. Additionally, we want the classifiers to recognize the particular type of sensitive topic with which they deal, in order for a company to have a better knowledge of the data they own. We expect to make the model described in this paper available as a web service, customized to private data of possible customers, or even in a free-to-use version based on the freely available data set we have built to train the classifiers. Full article

(This article belongs to the Special Issue Big Data Analytics, Privacy and Visualization)

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17 pages, 8706 KiB

Open AccessArticle

Research on Urban Traffic Incident Detection Based on Vehicle Cameras

by Zhuofei Xia, Jiayuan Gong, Hailong Yu, Wenbo Ren and Jingnan Wang

Future Internet 2022, 14(8), 227; https://doi.org/10.3390/fi14080227 - 26 Jul 2022

Cited by 2 | Viewed by 2398

Abstract

Situational detection in the traffic system is of great significance to traffic management and even urban management. Traditional detection methods are generally based on roadside equipment monitoring roads, and it is difficult to support large-scale and fine-grained traffic incident detection. In this study, [...] Read more.

Situational detection in the traffic system is of great significance to traffic management and even urban management. Traditional detection methods are generally based on roadside equipment monitoring roads, and it is difficult to support large-scale and fine-grained traffic incident detection. In this study, we propose a detection method applied to the mobile edge, which detects traffic incidents based on the video captured by vehicle cameras, so as to overcome the limitations of roadside terminal perception. For swarm intelligence detection, we propose an improved YOLOv5s object detection network, adding an atrous pyramid pooling layer to the network and introducing a fusion attention mechanism to improve the model accuracy. Compared with the raw YOLOv5s, the mAP metrics of our improved model are increased by 3.3% to 84.2%, enabling it to detect vehicles, pedestrians, traffic accidents, and fire traffic incidents on the road with high precision in real time. This provides information for city managers to help them grasp the abnormal operation status of roads and cities in a timely and effective manner. Full article

(This article belongs to the Special Issue Knowledge Graph Mining and Its Applications)

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19 pages, 3398 KiB

Open AccessArticle

Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning

by Zhiqiang Dai, Gaochao Xu, Ziqi Liu, Jiaqi Ge and Wei Wang

Future Internet 2022, 14(8), 226; https://doi.org/10.3390/fi14080226 - 26 Jul 2022

Cited by 6 | Viewed by 1954

Abstract

Unmanned aerial vehicles (UAVs) have the characteristics of portability, safety, and strong adaptability. In the case of a maritime disaster, they can be used for personnel search and rescue, real-time monitoring, and disaster assessment. However, the power, computing power, and other resources of [...] Read more.

Unmanned aerial vehicles (UAVs) have the characteristics of portability, safety, and strong adaptability. In the case of a maritime disaster, they can be used for personnel search and rescue, real-time monitoring, and disaster assessment. However, the power, computing power, and other resources of UAVs are often limited. Therefore, this paper combines a UAV and mobile edge computing (MEC), and designs a deep reinforcement learning-based online task offloading (DOTO) algorithm. The algorithm can obtain an online offloading strategy that maximizes the residual energy of the UAV by jointly optimizing the UAV’s time and communication resources. The DOTO algorithm adopts time division multiple access (TDMA) to offload and schedule the UAV computing task, integrates wireless power transfer (WPT) to supply power to the UAV, calculates the residual energy corresponding to the offloading action through the convex optimization method, and uses an adaptive

K

method to reduce the computational complexity of the algorithm. The simulation results show that the DOTO algorithm proposed in this paper for the energy-saving goal of maximizing the residual energy of UAVs in MEC can provide the UAV with an online task offloading strategy that is superior to other traditional benchmark schemes. In particular, when an individual UAV exits the system due to insufficient power or failure, or a new UAV is connected to the system, it can perform timely and automatic adjustment without manual participation, and has good stability and adaptability. Full article

(This article belongs to the Special Issue Edge AI: Applications of Edge Computing and Artificial Intelligence in IoT)

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16 pages, 2701 KiB

Open AccessArticle

Analysis and Visualization of New Energy Vehicle Battery Data

by Wenbo Ren, Xinran Bian, Jiayuan Gong, Anqing Chen, Ming Li, Zhuofei Xia and Jingnan Wang

Future Internet 2022, 14(8), 225; https://doi.org/10.3390/fi14080225 - 26 Jul 2022

Cited by 1 | Viewed by 2685

Abstract

In order to safely and efficiently use their power as well as to extend the life of Li-ion batteries, it is important to accurately analyze original battery data and quickly predict SOC. However, today, most of them are analyzed directly for SOC, and [...] Read more.

In order to safely and efficiently use their power as well as to extend the life of Li-ion batteries, it is important to accurately analyze original battery data and quickly predict SOC. However, today, most of them are analyzed directly for SOC, and the analysis of the original battery data and how to obtain the factors affecting SOC are still lacking. Based on this, this paper uses the visualization method to preprocess, clean, and parse collected original battery data (hexadecimal), followed by visualization and analysis of the parsed data, and finally the K-Nearest Neighbor (KNN) algorithm is used to predict the SOC. Through experiments, the method can completely analyze the hexadecimal battery data based on the GB/T32960 standard, including three different types of messages: vehicle login, real-time information reporting, and vehicle logout. At the same time, the visualization method is used to intuitively and concisely analyze the factors affecting SOC. Additionally, the KNN algorithm is utilized to identify the K value and P value using dynamic parameters, and the resulting mean square error (MSE) and test score are 0.625 and 0.998, respectively. Through the overall experimental process, this method can well analyze the battery data from the source, visually analyze various factors and predict SOC. Full article

(This article belongs to the Special Issue Knowledge Graph Mining and Its Applications)

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18 pages, 4078 KiB

Open AccessArticle

Augmenting Industrial Control Rooms with Multimodal Collaborative Interaction Techniques

by Jessica Rubart, Valentin Grimm and Jonas Potthast

Future Internet 2022, 14(8), 224; https://doi.org/10.3390/fi14080224 - 26 Jul 2022

Cited by 3 | Viewed by 1880

Abstract

The German manufacturing industry has been carrying out new developments towards the next industrial revolution, focusing on smart manufacturing environments. Our work emphasizes human-centered control rooms in the context of production plants. Increased automation does not have to come with less human control. [...] Read more.

The German manufacturing industry has been carrying out new developments towards the next industrial revolution, focusing on smart manufacturing environments. Our work emphasizes human-centered control rooms in the context of production plants. Increased automation does not have to come with less human control. Therefore, we report on multimodal collaborative interaction techniques to augment industrial control rooms. In particular, we include mobile workers who use the control room while being in the production hall using tablets or specifically mixed reality glasses. Collaborative annotation dashboards support discussions and a shared understanding among analysts. Manufacturing-related data can be integrated into business analytics environments so that holistic analyses can be performed. Multimodal interaction techniques can support effective interaction with the control room based on the users’ preferences. Immersive experience through mixed reality-based three-dimensional visualizations and interaction possibilities support users in obtaining a clear understanding of the underlying data. Full article

(This article belongs to the Special Issue Interface Design Challenges for Smart Control Rooms)

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11 pages, 1419 KiB

Open AccessArticle

A New Scheme for Detecting Malicious Nodes in Vehicular Ad Hoc Networks Based on Monitoring Node Behavior

by Muhsen Alkhalidy, Atalla Fahed Al-Serhan, Ayoub Alsarhan and Bashar Igried

Future Internet 2022, 14(8), 223; https://doi.org/10.3390/fi14080223 - 26 Jul 2022

Cited by 18 | Viewed by 2100

Abstract

Vehicular ad hoc networks have played a key role in intelligent transportation systems that considerably improve road safety and management. This new technology allows vehicles to communicate and share road information. However, malicious users may inject false emergency alerts into vehicular ad hoc [...] Read more.

Vehicular ad hoc networks have played a key role in intelligent transportation systems that considerably improve road safety and management. This new technology allows vehicles to communicate and share road information. However, malicious users may inject false emergency alerts into vehicular ad hoc networks, preventing nodes from accessing accurate road information. In order to assure the reliability and trustworthiness of information through the networks, assessing the credibility of nodes has become a critical task in vehicular ad hoc networks. A new scheme for malicious node detection is proposed in this work. Multiple factors are fed into a fuzzy logic model for evaluating the trust for each node. Vehicles are divided into clusters in our approach, and a road side unit manages each cluster. The road side unit assesses the credibility of nodes before accessing vehicular ad hoc networks. The road side unit evicts a malicious node based on trust value. Simulations are used to validate our technique. We demonstrate that our scheme can detect and evict all malicious nodes in the vehicular ad hoc network over time, lowering the ratio of malicious nodes. Furthermore, it has a positive impact on selfish node participation. The scheme increases the success rate of delivered data to the same level as the ideal cases when no selfish node is present. Full article

(This article belongs to the Special Issue Security for Vehicular Ad Hoc Networks)

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28 pages, 1016 KiB

Open AccessArticle

An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services

by Farhad Ahamed, Farnaz Farid, Basem Suleiman, Zohaib Jan, Luay A. Wahsheh and Seyed Shahrestani

Future Internet 2022, 14(8), 222; https://doi.org/10.3390/fi14080222 - 26 Jul 2022

Cited by 23 | Viewed by 4012

Abstract

With the advent of modern technologies, the healthcare industry is moving towards a more personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies collect and analyse data from persons in [...] Read more.

With the advent of modern technologies, the healthcare industry is moving towards a more personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies collect and analyse data from persons in care to alert relevant parties if any anomaly is detected in a patient’s regular pattern. However, such reliance on IoT devices to capture continuous data extends the attack surfaces and demands high-security measures. Both patients and devices need to be authenticated to mitigate a large number of attack vectors. The biometric authentication method has been seen as a promising technique in these scenarios. To this end, this paper proposes an AI-based multimodal biometric authentication model for single and group-based users’ device-level authentication that increases protection against the traditional single modal approach. To test the efficacy of the proposed model, a series of AI models are trained and tested using physiological biometric features such as ECG (Electrocardiogram) and PPG (Photoplethysmography) signals from five public datasets available in Physionet and Mendeley data repositories. The multimodal fusion authentication model shows promising results with 99.8% accuracy and an Equal Error Rate (EER) of 0.16. Full article

(This article belongs to the Special Issue Securing Future Internet with Computational Intelligence)

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19 pages, 548 KiB

Open AccessArticle

Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development

by Marco Ferretti, Ugo Fiore, Francesca Perla, Marcello Risitano and Salvatore Scognamiglio

Future Internet 2022, 14(8), 221; https://doi.org/10.3390/fi14080221 - 25 Jul 2022

Cited by 5 | Viewed by 2040

Abstract

Accurate forecasts of containerised freight volumes are unquestionably important for port terminal operators to organise port operations and develop business plans. They are also relevant for port authorities, regulators, and governmental agencies dealing with transportation. In a time when deep learning is in [...] Read more.

Accurate forecasts of containerised freight volumes are unquestionably important for port terminal operators to organise port operations and develop business plans. They are also relevant for port authorities, regulators, and governmental agencies dealing with transportation. In a time when deep learning is in the limelight, owing to a consistent strip of success stories, it is natural to apply it to the tasks of forecasting container throughput. Given the number of options, practitioners can benefit from the lessons learned in applying deep learning models to the problem. Coherently, in this work, we devise a number of multivariate predictive models based on deep learning, analysing and assessing their performance to identify the architecture and set of hyperparameters that prove to be better suited to the task, also comparing the quality of the forecasts with seasonal autoregressive integrated moving average models. Furthermore, an innovative representation of seasonality is given by means of an embedding layer that produces a mapping in a latent space, with the parameters of such mapping being tuned using the quality of the predictions. Finally, we present some managerial implications, also putting into evidence the research limitations and future opportunities. Full article

(This article belongs to the Special Issue Software Engineering and Data Science II)

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26 pages, 6629 KiB

Open AccessArticle

Exploring Distributed Deep Learning Inference Using Raspberry Pi Spark Cluster

by Nicholas James, Lee-Yeng Ong and Meng-Chew Leow

Future Internet 2022, 14(8), 220; https://doi.org/10.3390/fi14080220 - 25 Jul 2022

Cited by 5 | Viewed by 4169

Abstract

Raspberry Pi (Pi) is a versatile general-purpose embedded computing device that can be used for both machine learning (ML) and deep learning (DL) inference applications such as face detection. This study trials the use of a Pi Spark cluster for distributed inference in [...] Read more.

Raspberry Pi (Pi) is a versatile general-purpose embedded computing device that can be used for both machine learning (ML) and deep learning (DL) inference applications such as face detection. This study trials the use of a Pi Spark cluster for distributed inference in TensorFlow. Specifically, it investigates the performance difference between a 2-node Pi 4B Spark cluster and other systems, including a single Pi 4B and a mid-end desktop computer. Enhancements for the Pi 4B were studied and compared against the Spark cluster to identify the more effective method in increasing the Pi 4B’s DL performance. Three experiments involving DL inference, which in turn involve image classification and face detection tasks, were carried out. Results showed that enhancing the Pi 4B was faster than using a cluster as there was no significant performance difference between using the cluster and a single Pi 4B. The difference between the mid-end computer and a single Pi 4B was between 6 and 15 times in the experiments. In the meantime, enhancing the Pi 4B is the more effective approach for increasing the DL performance, and more work needs to be done for scalable distributed DL inference to eventuate. Full article

(This article belongs to the Special Issue Distributed Systems and Artificial Intelligence)

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35 pages, 9433 KiB

Open AccessArticle

A Smart Parking Solution by Integrating NB-IoT Radio Communication Technology into the Core IoT Platform

by Esad Kadusic, Natasa Zivic, Christoph Ruland and Narcisa Hadzajlic

Future Internet 2022, 14(8), 219; https://doi.org/10.3390/fi14080219 - 25 Jul 2022

Cited by 15 | Viewed by 4995

Abstract

With the emerging Internet of Things (IoT) technologies, the smart city paradigm has become a reality. Wireless low-power communication technologies (LPWAN) are widely used for device connection in smart homes, smart lighting, mitering, and so on. This work suggests a new approach to [...] Read more.

With the emerging Internet of Things (IoT) technologies, the smart city paradigm has become a reality. Wireless low-power communication technologies (LPWAN) are widely used for device connection in smart homes, smart lighting, mitering, and so on. This work suggests a new approach to a smart parking solution using the benefits of narrowband Internet of Things (NB-IoT) technology. NB-IoT is an LPWAN technology dedicated to sensor communication within 5G mobile networks. This paper proposes the integration of NB-IoT into the core IoT platform, enabling direct sensor data navigation to the IoT radio stations for processing, after which they are forwarded to the user application programming interface (API). Showcasing the results of our research and experiments, this work suggests the ability of NB-IoT technology to support geolocation and navigation services, as well as payment and reservation services for vehicle parking to make the smart parking solutions smarter. Full article

(This article belongs to the Collection Innovative People-Centered Solutions Applied to Industries, Cities and Societies)

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17 pages, 5313 KiB

Open AccessArticle

Understanding Learners’ Perception of MOOCs Based on Review Data Analysis Using Deep Learning and Sentiment Analysis

by Xieling Chen, Fu Lee Wang, Gary Cheng, Man-Kong Chow and Haoran Xie

Future Internet 2022, 14(8), 218; https://doi.org/10.3390/fi14080218 - 25 Jul 2022

Cited by 11 | Viewed by 2680

Abstract

Massive open online courses (MOOCs) have exploded in popularity; course reviews are important sources for exploring learners’ perceptions about different factors associated with course design and implementation. This study aims to investigate the possibility of automatic classification for the semantic content of MOOC [...] Read more.

Massive open online courses (MOOCs) have exploded in popularity; course reviews are important sources for exploring learners’ perceptions about different factors associated with course design and implementation. This study aims to investigate the possibility of automatic classification for the semantic content of MOOC course reviews to understand factors that can predict learners’ satisfaction and their perceptions of these factors. To do this, this study employs a quantitative research methodology based on sentiment analysis and deep learning. Learners’ review data from Class Central are analyzed to automatically identify the key factors related to course design and implementation and the learners’ perceptions of these factors. A total of 186,738 review sentences associated with 13 subject areas are analyzed, and consequently, seven course factors that learners frequently mentioned are found. These factors include: “Platforms and tools”, “Course quality”, “Learning resources”, “Instructor”, “Relationship”, “Process”, and “Assessment”. Subsequently, each factor is assigned a sentimental value using lexicon-driven methodologies, and the topics that can influence learners’ learning experiences the most are decided. In addition, learners’ perceptions across different topics and subjects are explored and discussed. The findings of this study contribute to helping MOOC instructors in tailoring course design and implementation to bring more satisfactory learning experiences for learners. Full article

(This article belongs to the Special Issue Affective Computing and Sentiment Analysis)

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15 pages, 524 KiB

Open AccessArticle

Detection of Obfuscated Malicious JavaScript Code

by Ammar Alazab, Ansam Khraisat, Moutaz Alazab and Sarabjot Singh

Future Internet 2022, 14(8), 217; https://doi.org/10.3390/fi14080217 - 22 Jul 2022

Cited by 21 | Viewed by 4402

Abstract

Websites on the Internet are becoming increasingly vulnerable to malicious JavaScript code because of its strong impact and dramatic effect. Numerous recent cyberattacks use JavaScript vulnerabilities, and in some cases employ obfuscation to conceal their malice and elude detection. To secure Internet users, [...] Read more.

Websites on the Internet are becoming increasingly vulnerable to malicious JavaScript code because of its strong impact and dramatic effect. Numerous recent cyberattacks use JavaScript vulnerabilities, and in some cases employ obfuscation to conceal their malice and elude detection. To secure Internet users, an adequate intrusion-detection system (IDS) for malicious JavaScript must be developed. This paper proposes an automatic IDS of obfuscated JavaScript that employs several features and machine-learning techniques that effectively distinguish malicious and benign JavaScript codes. We also present a new set of features, which can detect obfuscation in JavaScript. The features are selected based on identifying obfuscation, a popular method to bypass conventional malware detection systems. The performance of the suggested approach has been tested on JavaScript obfuscation attacks. The studies have shown that IDS based on selected features has a detection rate of 94% for malicious samples and 81% for benign samples within the dimension of the feature vector of 60. Full article

(This article belongs to the Section Big Data and Augmented Intelligence)

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Future Internet, Volume 14, Issue 8 (August 2022) – 28 articles

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