Future Internet

Open AccessArticle

A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning

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Future Internet 2023, 15(6), 209; https://doi.org/10.3390/fi15060209 - 08 Jun 2023

Abstract

As a new distributed machine learning (ML) approach, federated learning (FL) shows great potential to preserve data privacy by enabling distributed data owners to collaboratively build a global model without sharing their raw data. However, the heterogeneity in terms of data distribution and [...] Read more.

As a new distributed machine learning (ML) approach, federated learning (FL) shows great potential to preserve data privacy by enabling distributed data owners to collaboratively build a global model without sharing their raw data. However, the heterogeneity in terms of data distribution and hardware configurations make it hard to select participants from the thousands of nodes. In this paper, we propose a multi-objective node selection approach to improve time-to-accuracy performance while resisting malicious nodes. We firstly design a deep reinforcement learning-assisted FL framework. Then, the problem of multi-objective node selection under this framework is formulated as a Markov decision process (MDP), which aims to reduce the training time and improve model accuracy simultaneously. Finally, a Deep Q-Network (DQN)-based algorithm is proposed to efficiently solve the optimal set of participants for each iteration. Simulation results show that the proposed method not only significantly improves the accuracy and training speed of FL, but also has stronger robustness to resist malicious nodes. Full article

(This article belongs to the Section Internet of Things)

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Open AccessArticle

A Blockchain Self-Sovereign Identity for Open Banking Secured by the Customer’s Banking Cards

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Future Internet 2023, 15(6), 208; https://doi.org/10.3390/fi15060208 - 08 Jun 2023

Abstract

Open finance is evolving and extending open banking. This creates a large context that implies a financial and identity data exchange paradigm, which faces challenges to balance customer experience, security, and the self-control over personal identity information. We propose Self-Sovereign Banking Identity (SSBI), [...] Read more.

Open finance is evolving and extending open banking. This creates a large context that implies a financial and identity data exchange paradigm, which faces challenges to balance customer experience, security, and the self-control over personal identity information. We propose Self-Sovereign Banking Identity (SSBI), a Blockchain-based self-sovereign identity (SSI) to secure private data sharing by utilizing trusted customer’s banking cards as a key storage and identity transaction-signing enclave. The design and implementation of the SSI framework is based on the Veramo SDK and Ethereum to overcome the limitation of signing curve availability on the current banking Java Cards needed for Hyperledger Indy. SSBI uses the elliptic curve SECP256K1 for transaction signing, which exists for several payment cards in the market. SSBI enables automated financial services and trust in the service provider communication. This work analyzes the flow and framework components, and evaluates the usability, integration, and performance in terms of throughput, latency, security, and complexity. Furthermore, the proposed approach is compared with related solutions. The presented prototype implementation is based on a test Ethereum network and signing transactions on the banking card. The preliminary results show that SSBI provides an effective solution for integrating the customer’s banking cards to secure open banking identity exchange. Furthermore, it allows the integration of several scenarios to support trusted open banking. The Blockchain layer settings need to be scaled and improved before real-world implementation. Full article

(This article belongs to the Special Issue Security and Privacy in Blockchains and the IoT II)

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Open AccessArticle

S2NetM: A Semantic Social Network of Things Middleware for Developing Smart and Collaborative IoT-Based Solutions

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Antonios Pliatsios

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Dimitrios Lymperis

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Christos Goumopoulos

Future Internet 2023, 15(6), 207; https://doi.org/10.3390/fi15060207 - 06 Jun 2023

Abstract

The Social Internet of Things (SIoT) paradigm combines the benefits of social networks with IoT networks to create more collaborative and efficient systems, offering enhanced scalability, better navigability, flexibility, and dynamic decision making. However, SIoT also presents challenges related to dynamic friendship selection, [...] Read more.

The Social Internet of Things (SIoT) paradigm combines the benefits of social networks with IoT networks to create more collaborative and efficient systems, offering enhanced scalability, better navigability, flexibility, and dynamic decision making. However, SIoT also presents challenges related to dynamic friendship selection, privacy and security, interoperability, and standardization. To fully unlock the potential of SIoT, it is crucial to establish semantic interoperability between the various entities, applications, and networks that comprise the system. This paper introduces the Semantic Social Network of Things Middleware (S2NetM), which leverages social relationships to enhance semantic interoperability in SIoT systems. The S2NetM employs semantic reasoning and alignment techniques to facilitate the creation of dynamic, context-aware social networks of things that can collaboratively work together and enable new opportunities for IoT-based solutions. The main contributions of this paper are the specification of the S2NetM and the associated ontology, as well as the discussion of a case study demonstrating the effectiveness of the proposed solution. Full article

(This article belongs to the Special Issue Semantic and Social Internet of Things)

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Open AccessArticle

Anomaly Detection for Hydraulic Power Units—A Case Study

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Paweł Fic

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Adam Czornik

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Piotr Rosikowski

Future Internet 2023, 15(6), 206; https://doi.org/10.3390/fi15060206 - 02 Jun 2023

Abstract

This article aims to present the real-world implementation of an anomaly detection system of a hydraulic power unit. Implementation involved the Internet of Things approach. A detailed description of the system architecture is provided. The complete path from sensors through PLC and the [...] Read more.

This article aims to present the real-world implementation of an anomaly detection system of a hydraulic power unit. Implementation involved the Internet of Things approach. A detailed description of the system architecture is provided. The complete path from sensors through PLC and the edge computer to the cloud is presented. Some technical information about hydraulic power units is also given. This article involves the description of several model-at-scale deployment techniques. In addition, the approach to the synthesis of anomaly and novelty detection models was described. Anomaly detection of data acquired from the hydraulic power unit was carried out using two approaches, statistical and black-box, involving the One Class SVM model. The costs of cloud resources and services that were generated in the project are presented. Since the article describes a commercial implementation, the results have been presented as far as the formal and business conditions allow. Full article

(This article belongs to the Special Issue Edge and Fog Computing for the Internet of Things)

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Open AccessReview

2D Semantic Segmentation: Recent Developments and Future Directions

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Future Internet 2023, 15(6), 205; https://doi.org/10.3390/fi15060205 - 01 Jun 2023

Abstract

Semantic segmentation is a critical task in computer vision that aims to assign each pixel in an image a corresponding label on the basis of its semantic content. This task is commonly referred to as dense labeling because it requires pixel-level classification of [...] Read more.

Semantic segmentation is a critical task in computer vision that aims to assign each pixel in an image a corresponding label on the basis of its semantic content. This task is commonly referred to as dense labeling because it requires pixel-level classification of the image. The research area of semantic segmentation is vast and has achieved critical advances in recent years. Deep learning architectures in particular have shown remarkable performance in generating high-level, hierarchical, and semantic features from images. Among these architectures, convolutional neural networks have been widely used to address semantic segmentation problems. This work aims to review and analyze recent technological developments in image semantic segmentation. It provides an overview of traditional and deep-learning-based approaches and analyzes their structural characteristics, strengths, and limitations. Specifically, it focuses on technical developments in deep-learning-based 2D semantic segmentation methods proposed over the past decade and discusses current challenges in semantic segmentation. The future development direction of semantic segmentation and the potential research areas that need further exploration are also examined. Full article

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Open AccessArticle

Avoiding Detection by Hostile Nodes in Airborne Tactical Networks

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Dragos Ilie

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Håkan Grahn

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Lars Lundberg

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Alexander Westerhagen

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Bo Granbom

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Anders Höök

Future Internet 2023, 15(6), 204; https://doi.org/10.3390/fi15060204 - 31 May 2023

Abstract

Contemporary airborne radio networks are usually implemented using omnidirectional antennas. Unfortunately, such networks suffer from disadvantages such as easy detection by hostile aircraft and potential information leakage. In this paper, we present a novel mobile ad hoc network (MANET) routing protocol based on [...] Read more.

Contemporary airborne radio networks are usually implemented using omnidirectional antennas. Unfortunately, such networks suffer from disadvantages such as easy detection by hostile aircraft and potential information leakage. In this paper, we present a novel mobile ad hoc network (MANET) routing protocol based on directional antennas and situation awareness data that utilizes adaptive multihop routing to avoid sending information in directions where hostile nodes are present. Our protocol is implemented in the OMNEST simulator and evaluated using two realistic flight scenarios involving 8 and 24 aircraft, respectively. The results show that our protocol has significantly fewer leaked packets than comparative protocols, but at a slightly higher cost in terms of longer packet lifetime. Full article

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Open AccessReview

Federated Learning and Blockchain Integration for Privacy Protection in the Internet of Things: Challenges and Solutions

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Muneerah Al Asqah

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Tarek Moulahi

Future Internet 2023, 15(6), 203; https://doi.org/10.3390/fi15060203 - 31 May 2023

Abstract

The Internet of Things (IoT) compromises multiple devices connected via a network to perform numerous activities. The large amounts of raw user data handled by IoT operations have driven researchers and developers to provide guards against any malicious threats. Blockchain is a technology [...] Read more.

The Internet of Things (IoT) compromises multiple devices connected via a network to perform numerous activities. The large amounts of raw user data handled by IoT operations have driven researchers and developers to provide guards against any malicious threats. Blockchain is a technology that can give connected nodes means of security, transparency, and distribution. IoT devices could guarantee data centralization and availability with shared ledger technology. Federated learning (FL) is a new type of decentralized machine learning (DML) where clients collaborate to train a model and share it privately with an aggregator node. The integration of Blockchain and FL enabled researchers to apply numerous techniques to hide the shared training parameters and protect their privacy. This study explores the application of this integration in different IoT environments, collectively referred to as the Internet of X (IoX). In this paper, we present a state-of-the-art review of federated learning and Blockchain and how they have been used in collaboration in the IoT ecosystem. We also review the existing security and privacy challenges that face the integration of federated learning and Blockchain in the distributed IoT environment. Furthermore, we discuss existing solutions for security and privacy by categorizing them based on the nature of the privacy-preservation mechanism. We believe that our paper will serve as a key reference for researchers interested in improving solutions based on mixing Blockchain and federated learning in the IoT environment while preserving privacy. Full article

(This article belongs to the Special Issue Blockchain Security and Privacy II)

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Open AccessArticle

PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks

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Ernesto Cadena Muñoz

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Gustavo Chica Pedraza

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Rafael Cubillos-Sánchez

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Alexander Aponte-Moreno

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Mónica Espinosa Buitrago

Future Internet 2023, 15(6), 202; https://doi.org/10.3390/fi15060202 - 31 May 2023

Abstract

The primary user emulation (PUE) attack is one of the strongest attacks in mobile cognitive radio networks (MCRN) because the primary users (PU) and secondary users (SU) are unable to communicate if a malicious user (MU) is present. In the literature, some techniques [...] Read more.

The primary user emulation (PUE) attack is one of the strongest attacks in mobile cognitive radio networks (MCRN) because the primary users (PU) and secondary users (SU) are unable to communicate if a malicious user (MU) is present. In the literature, some techniques are used to detect the attack. However, those techniques do not explore the cooperative detection of PUE attacks using deep neural networks (DNN) in one MCRN network and with experimental results on software-defined radio (SDR). In this paper, we design and implement a PUE attack in an MCRN, including a countermeasure based on the entropy of the signals, DNN, and cooperative spectrum sensing (CSS) to detect the attacks. A blacklist is included in the fusion center (FC) to record the data of the MU. The scenarios are simulated and implemented on the SDR testbed. Results show that this solution increases the probability of detection (PD) by 20% for lower signal noise ratio (SNR) values, allowing the detection of the PUE attack and recording the data for future reference by the attacker, sharing the data for all the SU. Full article

(This article belongs to the Special Issue Artificial Intelligence and IoT Security: Opportunities and Challenges)

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Open AccessReview

Methods of Annotating and Identifying Metaphors in the Field of Natural Language Processing

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Martina Ptiček

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Jasminka Dobša

Future Internet 2023, 15(6), 201; https://doi.org/10.3390/fi15060201 - 31 May 2023

Abstract

Metaphors are an integral and important part of human communication and greatly impact the way our thinking is formed and how we understand the world. The theory of the conceptual metaphor has shifted the focus of research from words to thinking, and also [...] Read more.

Metaphors are an integral and important part of human communication and greatly impact the way our thinking is formed and how we understand the world. The theory of the conceptual metaphor has shifted the focus of research from words to thinking, and also influenced research of the linguistic metaphor, which deals with the issue of how metaphors are expressed in language or speech. With the development of natural language processing over the past few decades, new methods and approaches to metaphor identification have been developed. The aim of the paper is to map the methods of annotating and identifying metaphors in the field of natural language processing and to give a systematic overview of how relevant linguistic theories and natural language processing intersect. The paper provides an outline of cognitive linguistic metaphor theory and an overview of relevant methods of annotating linguistic and conceptual metaphors as well as publicly available datasets. Identification methods are presented chronologically, from early approaches and hand-coded knowledge to statistical methods of machine learning and contemporary methods of using neural networks and contextual word embeddings. Full article

(This article belongs to the Special Issue Deep Learning and Natural Language Processing II)

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Open AccessReview

Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review

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Shereen Ismail

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Diana W. Dawoud

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Hassan Reza

Future Internet 2023, 15(6), 200; https://doi.org/10.3390/fi15060200 - 30 May 2023

Abstract

As an Internet of Things (IoT) technological key enabler, Wireless Sensor Networks (WSNs) are prone to different kinds of cyberattacks. WSNs have unique characteristics, and have several limitations which complicate the design of effective attack prevention and detection techniques. This paper aims to [...] Read more.

As an Internet of Things (IoT) technological key enabler, Wireless Sensor Networks (WSNs) are prone to different kinds of cyberattacks. WSNs have unique characteristics, and have several limitations which complicate the design of effective attack prevention and detection techniques. This paper aims to provide a comprehensive understanding of the fundamental principles underlying cybersecurity in WSNs. In addition to current and envisioned solutions that have been studied in detail, this review primarily focuses on state-of-the-art Machine Learning (ML) and Blockchain (BC) security techniques by studying and analyzing 164 up-to-date publications highlighting security aspect in WSNs. Then, the paper discusses integrating BC and ML towards developing a lightweight security framework that consists of two lines of defence, i.e, cyberattack detection and cyberattack prevention in WSNs, emphasizing the relevant design insights and challenges. The paper concludes by presenting a proposed integrated BC and ML solution highlighting potential BC and ML algorithms underpinning a less computationally demanding solution. Full article

(This article belongs to the Special Issue Security and Privacy in Blockchains and the IoT II)

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Open AccessArticle

Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

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Future Internet 2023, 15(6), 199; https://doi.org/10.3390/fi15060199 - 30 May 2023

Abstract

Cyber-physical systems (CPS) refer to systems that integrate communication, control, and computational elements into physical processes to facilitate the control of physical systems and effective monitoring. The systems are designed to interact with the physical world, monitor and control the physical processes while [...] Read more.

Cyber-physical systems (CPS) refer to systems that integrate communication, control, and computational elements into physical processes to facilitate the control of physical systems and effective monitoring. The systems are designed to interact with the physical world, monitor and control the physical processes while in operation, and generate data. Deep Neural Networks (DNN) comprise multiple layers of interconnected neurons that process input data to produce predictions. Spatial-temporal data represents the physical world and its evolution over time and space. The generated spatial-temporal data is used to make decisions and control the behavior of CPS. This paper systematically reviews the applications of DNNs, namely convolutional, recurrent, and graphs, in handling spatial-temporal data in CPS. An extensive literature survey is conducted to determine the areas in which DNNs have successfully captured spatial-temporal data in CPS and the emerging areas that require attention. The research proposes a three-dimensional framework that considers: CPS (transportation, manufacturing, and others), Target (spatial-temporal data processing, anomaly detection, predictive maintenance, resource allocation, real-time decisions, and multi-modal data fusion), and DNN schemes (CNNs, RNNs, and GNNs). Finally, research areas that need further investigation are identified, such as performance and security. Addressing data quality, strict performance assurance, reliability, safety, and security resilience challenges are the areas that are required for further research. Full article

(This article belongs to the Section Internet of Things)

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Open AccessArticle

Synchronizing Many Filesystems in Near Linear Time

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Elod P. Csirmaz

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Laszlo Csirmaz

Future Internet 2023, 15(6), 198; https://doi.org/10.3390/fi15060198 - 30 May 2023

Abstract

Finding a provably correct subquadratic synchronization algorithm for many filesystem replicas is one of the main theoretical problems in operational transformation (OT) and conflict-free replicated data types (CRDT) frameworks. Based on the algebraic theory of filesystems, which incorporates non-commutative filesystem commands natively, we [...] Read more.

Finding a provably correct subquadratic synchronization algorithm for many filesystem replicas is one of the main theoretical problems in operational transformation (OT) and conflict-free replicated data types (CRDT) frameworks. Based on the algebraic theory of filesystems, which incorporates non-commutative filesystem commands natively, we developed and built a proof-of-concept implementation of an algorithm suite which synchronizes an arbitrary number of replicas. The result is provably correct, and the synchronized system is created in linear space and time after an initial sorting phase. It works by identifying conflicting command pairs and requesting one of the commands to be removed. The method can be guided to reach any of the theoretically possible synchronized states. The algorithm also allows asynchronous usage. After the client sends a synchronization request, the local replica remains available for further modifications. When the synchronization instructions arrive, they can be merged with the changes made since the synchronization request. The suite also works on filesystems with a directed acyclic graph-based path structure in place of the traditional tree-like arrangement. Consequently, our algorithms apply to filesystems with hard or soft links as long as the links create no loops. Full article

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

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Open AccessArticle

How Can We Achieve Query Keyword Frequency Analysis in Privacy-Preserving Situations?

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Future Internet 2023, 15(6), 197; https://doi.org/10.3390/fi15060197 - 29 May 2023

Abstract

Recently, significant progress has been made in the field of public key encryption with keyword search (PEKS), with a focus on optimizing search methods and improving the security and efficiency of schemes. Keyword frequency analysis is a powerful tool for enhancing retrieval services [...] Read more.

Recently, significant progress has been made in the field of public key encryption with keyword search (PEKS), with a focus on optimizing search methods and improving the security and efficiency of schemes. Keyword frequency analysis is a powerful tool for enhancing retrieval services in explicit databases. However, designing a PEKS scheme that integrates keyword frequency analysis while preserving privacy and security has remained challenging, as it may conflict with some of the security principles of PEKS. In this paper, we propose an innovative scheme that introduces a security deadline to query trapdoors through the use of timestamps. This means that the keywords in the query trapdoor can only be recovered after the security deadline has passed. This approach allows for keyword frequency analysis of query keywords without compromising data privacy and user privacy, while also providing protection against keyword-guessing attacks through the dual-server architecture of our scheme. Moreover, our scheme supports multi-keyword queries in multi-user scenarios and is highly scalable. Finally, we evaluate the computational and communication efficiency of our scheme, demonstrating its feasibility in practical applications. Full article

(This article belongs to the Special Issue Cryptography in Digital Networks)

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Open AccessReview

Task Automation Intelligent Agents: A Review

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Abdul Wali

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Saipunidzam Mahamad

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Suziah Sulaiman

Future Internet 2023, 15(6), 196; https://doi.org/10.3390/fi15060196 - 29 May 2023

Abstract

As technological advancements increase exponentially, mobile phones become smarter with machine learning and artificial intelligence algorithms. These advancements have allowed mobile phone users to perform most of their daily routine tasks on mobile phones; tasks performed in daily routines are called repetitive tasks [...] Read more.

As technological advancements increase exponentially, mobile phones become smarter with machine learning and artificial intelligence algorithms. These advancements have allowed mobile phone users to perform most of their daily routine tasks on mobile phones; tasks performed in daily routines are called repetitive tasks and are performed manually by the users themselves. However, machine learning and artificial intelligence have enabled those tasks to be performed automatically, known as task automation. The users can perform task automation, e.g., through creating automation rules or an intelligent agent, e.g., conversational agents, virtual personal assistants, etc. Several techniques to achieve task automation have been proposed, but this review shows that task automation by programming by demonstration has had massive developmental growth because of its user-centered approach. Apple Siri, Google Assistant, MS Cortana, and Amazon Alexa are the most known task automation agents. However, these agents are not widely adopted because of their usability issues. In this study, two research questions are evaluated through the available literature to expand the research on intelligent task automation agents: (1) What is the state-of-the-art in task automation agents? (2) What are the existing methods and techniques for developing usability heuristics, specifically for intelligent agents? Research shows groundbreaking developments have been made in mobile phone task automation recently. However, it must still be conducted per usability principles to achieve maximum usability and user satisfaction. The second research question further justifies developing a set of domain-specific usability heuristics for mobile task automation intelligent agents. Full article

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Open AccessArticle

Feature Construction Using Persistence Landscapes for Clustering Noisy IoT Time Series

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Renjie Chen

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Nalini Ravishanker

Future Internet 2023, 15(6), 195; https://doi.org/10.3390/fi15060195 - 28 May 2023

Abstract

With the advancement of IoT technologies, there is a large amount of data available from wireless sensor networks (WSN), particularly for studying climate change. Clustering long and noisy time series has become an important research area for analyzing this data. This paper proposes [...] Read more.

With the advancement of IoT technologies, there is a large amount of data available from wireless sensor networks (WSN), particularly for studying climate change. Clustering long and noisy time series has become an important research area for analyzing this data. This paper proposes a feature-based clustering approach using topological data analysis, which is a set of methods for finding topological structure in data. Persistence diagrams and landscapes are popular topological summaries that can be used to cluster time series. This paper presents a framework for selecting an optimal number of persistence landscapes, and using them as features in an unsupervised learning algorithm. This approach reduces computational cost while maintaining accuracy. The clustering approach was demonstrated to be accurate on simulated data, based on only four, three, and three features, respectively, selected in Scenarios 1–3. On real data, consisting of multiple long temperature streams from various US locations, our optimal feature selection method achieved approximately a 13 times speed-up in computing. Full article

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

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Open AccessArticle

Research and Design of a Decentralized Edge-Computing-Assisted LoRa Gateway

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Future Internet 2023, 15(6), 194; https://doi.org/10.3390/fi15060194 - 27 May 2023

Abstract

As a narrowband communication technology, long-range (LoRa) contributes to the long development of Internet of Things (IoT) applications. The LoRa gateway plays an important role in the IoT transport layer, and security and efficiency are the key issues of the current research. In [...] Read more.

As a narrowband communication technology, long-range (LoRa) contributes to the long development of Internet of Things (IoT) applications. The LoRa gateway plays an important role in the IoT transport layer, and security and efficiency are the key issues of the current research. In the centralized working model of IoT systems built by traditional LoRa gateways, all the data generated and reported by end devices are processed and stored in cloud servers, which are susceptible to security issues such as data loss and data falsification. Edge computing (EC), as an innovative approach that brings data processing and storage closer to the endpoints, can create a decentralized security infrastructure for LoRa gateway systems, resulting in an EC-assisted IoT working model. Although this paradigm delivers unique features and an improved quality of service (QoS), installing IoT applications at LoRa gateways with limited computing and memory capabilities presents considerable obstacles. This article proposes the design and implementation of an “EC-assisted LoRa gateway” using edge computing. Our proposed latency-aware algorithm (LAA) can greatly improve the reliability of the network system by using a distributed edge computing network technology that can achieve maintenance operations, such as detection, repair, and replacement of failures of edge nodes in the network. Then, an EC-assisted LoRa gateway prototype was developed on an embedded hardware system. Finally, experiments were conducted to evaluate the performance of the proposed EC-assisted LoRa gateway. Compared with the conventional LoRa gateway, the proposed edge intelligent LoRa gateway had 41.1% lower bandwidth utilization and handled more end devices, ensuring system availability and IoT network reliability more effectively. Full article

(This article belongs to the Special Issue Distributing Computing in Internet of Things)

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Open AccessArticle

Deep Learning-Based Symptomizing Cyber Threats Using Adaptive 5G Shared Slice Security Approaches

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Future Internet 2023, 15(6), 193; https://doi.org/10.3390/fi15060193 - 26 May 2023

Abstract

In fifth Generation (5G) networks, protection from internal attacks, external breaches, violation of confidentiality, and misuse of network vulnerabilities is a challenging task. Various approaches, especially deep-learning (DL) prototypes, have been adopted in order to counter such challenges. For 5G network defense, DL [...] Read more.

In fifth Generation (5G) networks, protection from internal attacks, external breaches, violation of confidentiality, and misuse of network vulnerabilities is a challenging task. Various approaches, especially deep-learning (DL) prototypes, have been adopted in order to counter such challenges. For 5G network defense, DL module are recommended here in order to symptomize suspicious NetFlow data. This module behaves as a virtual network function (VNF) and is placed along a 5G network. The DL module as a cyber threat-symptomizing (CTS) unit acts as a virtual security scanner along the 5G network data analytic function (NWDAF) to monitor the network data. When the data were found to be suspicious, causing network bottlenecks and let-downs of end-user services, they were labeled as “Anomalous”. For the best proactive and adaptive cyber defense system (PACDS), a logically organized modular approach has been followed to design the DL security module. In the application context, improvements have been made to input features dimension and computational complexity reduction with better response times and accuracy in outlier detection. Moreover, key performance indicators (KPIs) have been proposed for security module placement to secure interslice and intraslice communication channels from any internal or external attacks, also suggesting an adaptive defense mechanism and indicating its placement on a 5G network. Among the chosen DL models, the CNN model behaves as a stable model during behavior analysis in the results. The model classifies botnet-labeled data with 99.74% accuracy and higher precision. Full article

(This article belongs to the Special Issue 5G Security: Challenges, Opportunities, and the Road Ahead)

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Open AccessReview

ChatGPT and Open-AI Models: A Preliminary Review

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Konstantinos I. Roumeliotis

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Nikolaos D. Tselikas

Future Internet 2023, 15(6), 192; https://doi.org/10.3390/fi15060192 - 26 May 2023

Abstract

According to numerous reports, ChatGPT represents a significant breakthrough in the field of artificial intelligence. ChatGPT is a pre-trained AI model designed to engage in natural language conversations, utilizing sophisticated techniques from Natural Language Processing (NLP), Supervised Learning, and Reinforcement Learning to comprehend [...] Read more.

According to numerous reports, ChatGPT represents a significant breakthrough in the field of artificial intelligence. ChatGPT is a pre-trained AI model designed to engage in natural language conversations, utilizing sophisticated techniques from Natural Language Processing (NLP), Supervised Learning, and Reinforcement Learning to comprehend and generate text comparable to human-generated text. This article provides an overview of the training process and fundamental functionality of ChatGPT, accompanied by a preliminary review of the relevant literature. Notably, this article presents the first comprehensive literature review of this technology at the time of publication, aiming to aggregate all the available pertinent articles to facilitate further developments in the field. Ultimately, the authors aim to offer an appraisal of the technology’s potential implications on existing knowledge and technology, along with potential challenges that must be addressed. Full article

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

Open AccessArticle

In-Depth Co-Design of Mental Health Monitoring Technologies by People with Lived Experience

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The Consumer and Carer Advisory Group (CCAG)

Future Internet 2023, 15(6), 191; https://doi.org/10.3390/fi15060191 - 25 May 2023

Abstract

Advancements in digital monitoring solutions collaborate closely with electronic medical records. These fine-grained monitoring capacities can generate and process extensive electronic record data. Such capacities promise to enhance mental health care but also risk contributing to further stigmatization, prejudicial decision-making, and fears of [...] Read more.

Advancements in digital monitoring solutions collaborate closely with electronic medical records. These fine-grained monitoring capacities can generate and process extensive electronic record data. Such capacities promise to enhance mental health care but also risk contributing to further stigmatization, prejudicial decision-making, and fears of disempowerment. This article discusses the problems and solutions identified by nine people with lived experience of being mental health care consumers or informal carers. Over the course of ten facilitated focus group format sessions (two hours) between October 2019 and April 2021, the participants shared their lived experience of mental health challenges, care, and recovery within the Australian context. To support the development, design, and implementation of monitoring technologies, problems, and solutions were outlined in the following areas—access, agency, interactions with medical practitioners, medication management, and self-monitoring. Emergent design insights include recommendations for strengthened consent procedures, flexible service access options, and humanized consumer interactions. While consumers and carers saw value in digital monitoring technologies that could enable them to take on a more proactive involvement in their personal wellness, they had questions about their level of access to such services and expressed concerns about the changes to interactions with health professionals that might emerge from these digitally enabled processes. Full article

(This article belongs to the Special Issue Challenges and Opportunities in Electronic Medical Record (EMR))

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