International Journal of Network Management最新文献

Fractional non-fungible tokens (NFTs) have emerged at the forefront of blockchain innovation, merging tokenization, NFTs, and fractional ownership to democratize access to high-value digital assets. In this paper, we explore the fundamental concepts of blockchain technology, smart contracts, NFTs, and tokenization to lay the groundwork for understanding fractional NFTs. We investigate key ERC standards, including ERC-20, ERC-721, and ERC-1155, which are pivotal in enabling the creation and management of fractional NFTs on the Ethereum blockchain. Then, we present two major processes in fractional NFTs, minting and reconstitution. We develop fractional NFTs based on ERC standards and evaluate their gas consumption. Furthermore, through a comprehensive review of existing platforms, we analyze their minting and reconstitution processes and underlying ERC standards. Challenges, such as regulatory compliance and security, are also examined. We highlight the significance of robust security measures and transparency to build trust in fractional NFT ecosystems. While the field is still evolving, fractional NFTs have the potential to disrupt traditional ownership models and revolutionize industries. We envision fractional NFTs fostering a more inclusive and decentralized digital economy as technology advances and adoption grows.

In the past few years, cryptocurrency has gained widespread acceptance because of its decentralized nature, quick and secure transactions, and potential for investment and speculation. But the increased popularity has also led to increased cryptocurrency fraud, including scams, phishing attacks, Ponzi schemes, and other criminal activities. Although there is little documentation of cryptocurrency fraud, an in-depth study is essential to recognize various scams in different cryptocurrencies. To fill this gap, a study investigated cryptocurrency-related fraud in various cryptocurrencies and provided a taxonomy of crypto-forensics and forensic blockchain. In addition, we have introduced an architecture that integrates artificial intelligence (AI) and blockchain technologies to investigate and protect against instances of cryptocurrency fraud. The suggested design's effectiveness was evaluated using several machine learning (ML) classification algorithms. The conclusion of the evaluation confirmed that the random forest (RF) classifier performed the best, delivering the highest level of accuracy, that is, 97.5%. Once the ML classifiers detect cryptocurrency fraud, the information is securely stored in the InterPlanetary File System (IPFS); the document's hash is also stored in the blockchain using smart contracts. Law enforcement can leverage blockchain technology to secure access to fraudulent cryptographic transactions. The proposed architecture was tested for bandwidth utilization. Despite the potential benefits of blockchain and crypto-forensics, several issues and challenges remain, including privacy concerns, standardization, and difficulty identifying fraud between crypto-currencies. Finally, the paper discusses various problems and challenges in blockchain and crypto forensics to investigate cryptocurrency fraud.

Software defined network (SDN) integrated vehicular ad hoc network (VANET) is a magnificent technique for smart transportation as it raises the efficiency, safety, manageability, and comfort of traffic. SDN-integrated VANET (SDN-int-VANET) has numerous benefits, but it is susceptible to threats like distributed denial of service (DDoS). Several methods were suggested for DDoS attack detection (AD), but the existing approaches to optimization have given a base for enhancing the parameters. An incorrect selection of parameters results in a poor performance and poor fit to the data. To overcome these issues, residual-based temporal attention red fox-convolutional neural network (RTARF-CNN) for detecting DDoS attacks in SDN-int-VANET is introduced in this manuscript. The input data is taken from the SDN DDoS attack dataset. For restoring redundancy and missing value, developed random forest and local least squares (DRFLLS) are applied. Then the important features are selected from the pre-processed data with the help of stacked contractive autoencoders (St-CAE), which reduces the processing time of the introduced method. The selected features are classified by residual-based temporal attention-convolutional neural network (RTA-CNN). The weight parameter of RTA-CNN is optimized with the help of red fox optimization (RFO) for better classification. The introduced method is implemented in the PYTHON platform. The RTARF-CNN attains 99.8% accuracy, 99.5% sensitivity, 99.80% precision, and 99.8% specificity. The effectiveness of the introduced technique is compared with the existing approaches.

The integration of the Internet of Things (IoT) with blockchain technology has enabled a significant digital transformation in the areas of E-health, supply chain, financial services, smart grid, and automated contracts. Many E-health organizations take advantage of the game-changing power of blockchain and IoT to improve patient outcomes and optimize internal operational activities. In particular, it proposes a decentralized and evolutive way to model and acknowledge trust and data validity in a peer-to-peer network. Blockchain promises transparent and secure systems to provide new business solutions, especially when combined with smart contracts. In this paper, we provide a comprehensive survey of the literature involving blockchain technology applied to E-health. First, we present a brief background on blockchain and its fundamentals. Second, we review the opportunities and challenges of blockchain in the context of E-health. We then discuss popular consensus algorithms and smart contracts in blockchain in conjunction with E-health. Finally, blockchain platforms are evaluated for their suitability in the realm of IoT-based E-health, including electronic health records, electronic management records, and personal health records, from the perspective of remote patient monitoring.

The Ripple network is one typical blockchain-based decentralized credit network, which supports money transfer without physical money movement by only transferring the credits between participants. It is critical to obtain a deep understanding on the characteristics of the payment networks while optimizing the network design and transaction routing. This paper presents a comprehensive analysis to the Ripple transaction network, including two subnets formed by the two key functionalities, that is, Ripple Direct Payment Network (RDPN) and Ripple Credit Payment Network (RCPN). The analysis is performed with different network metrics, including clustering coefficient, centrality, and so on. Furthermore, this paper provides an in-depth analysis on the node degrees and edge weights, which reflect the number of transacted accounts of an account and the number of transactions between two accounts. The results show that the network is highly imbalanced and concentrated with a few nodes and edges holding most of the resources. Moreover, RDPN and RCPN show different characteristics in terms of transmitted and received transactions, the senders are more concentrated in RDPN, whereas in RCPN, the receivers are more concentrated.

In terms of cyber security, log files represent a rich source of information regarding the state of a computer service/system. Automating the process of summarizing log file content represents an important aid for decision-making, especially given the 24/7 nature of network/service operations. We perform benchmarking over eight distinct log files in order to assess the impact of the following: (1) different embedding methods for developing semantic descriptions of the original log files, (2) applying dimension reduction to the high-dimensional semantic space, and (3) assessing the impact of using different unsupervised learning algorithms for providing a visual summary of the service state. Benchmarking demonstrates that (1) word-to-vector embeddings identified by bidirectional encoder representation from transformers (BERT) without “fine-tuning” are sufficient to match the performance of Bag-or-Words embeddings provided by term frequency-inverse document frequency (TF-IDF) and (2) the self-organizing map without dimension reduction provides the most effective anomaly detector.

Network function virtualization (NFV) implements network functions as software, which enables flexible, resource-efficient, and rapid provision of network services. In NFV, network services are realized by the service function chain (SFC), which is a chain of virtual network functions (VNFs) linked in the proper order. Both availability and deployment cost are key concerns for network operators providing network services as SFC. This paper proposes a flexible VNF placement model on a per-VNF instance basis that minimizes deployment costs while satisfying availability requirements that may be placed on SFC. This paper uses a multidimensional universal generating function (MUGF) method, which is a multistate system analysis method, to compute the availability of a multistate SFC system with multiple VNFs coexisting on a server. The MUGF method calculates the performance of the entire SFC by combining the performance of servers as determined by applying a continuous-time Markov chain. To reduce the time to compute the SFC availability, we introduce operators to be applied to MUGF and develop an availability computing method. In addition, a heuristic algorithm for determining VNF placement targeting the lowest deployment cost possible while meeting availability requirements is presented. Numerical results show that the proposed model obtains VNF placement with lower cost than the conventional model in all examined cases. The proposed model achieves VNF placement at 58.5%–75.0% of the deployment cost of the conventional model for the same SFC availability requirements.

Cyber-physical systems (CPS) and the Internet of Things (IoT) technologies link urban systems through networks and improve the delivery of quality services to residents. To enhance municipality services, information and communication technologies (ICTs) are integrated with urban systems. However, the large number of sensors in a smart city generates a significant amount of delicate data, like medical records, credit card numerics, and location coordinates, which are transported across a network to data centers for analysis and processing. This makes smart cities vulnerable to cyberattacks because of the resource constraints of their technology infrastructure. Applications for smart cities pose many security challenges, such as zero-day attacks resulting from exploiting weaknesses in various protocols. Therefore, this paper proposes an optimal hybrid transit search-cascade regional convolutional neural network (hybrid TS-Cascade R-CNN) to detect cyberattacks. The proposed model combines the hybrid transit-search approach with the cascade regional convolutional neural network to create an optimal solution for cyberattack detection. The cascade regional convolutional network uses a hybrid transit search algorithm to enhance the effectiveness of cyberattack detection. By integrating these two approaches, the system can leverage both global traffic patterns and local indicators to improve the accuracy of attack detection. During the training process, the proposed model recognizes and classifies malicious input even in the presence of sophisticated attacks. Finally, the experimental analysis is carried out for various attacks based on different metrics. The accuracy rate attained by the proposed approach is 99.2%, which is acceptable according to standards.

Ubiquitous edge computing facilitates efficient cloud services near mobile devices, enabling mobile edge computing (MEC) to offer services more efficiently by presenting storage and processing capability within the proximity of mobile devices and in general IoT domains. However, compared with conventional mobile cloud computing, ubiquitous MEC introduces numerous complex challenges due to the heterogeneous smart devices, network infrastructures, and limited transmission bandwidth. Processing and managing such massive volumes of data generated from these devices is complex and challenging in edge infrastructures. On the other side, time-critical applications have stringent requirements such as ultra-low-latency, energy cost, mobility, resource, and security issues that cannot be neglected. For example, smart healthcare or industrial networks generate emergency information very frequently (i.e., often in terms of milliseconds), which needs to be processed near the sensing devices with minimal processing delay. In this context, future generation IoT requires robust and intelligent network management approaches that can handle the system complexity (e.g., scalability and orchestration) with little or no little human intervention and offer a better service to end-users. More precisely, AI/ML approaches designed explicitly for networks under high traffic volume of data help overcome several management challenges, such as (i) improving performance by balancing load and traffic, (ii) distributing the bandwidth spectrum based on demand, and (iii) traffic predictions. Moreover, this need also opens several new research directions such as new MEC architecture, service provisioning technique, security mechanisms, advanced 5G or beyond communication technology, ambient intelligence, and AI/ML-based solutions.

This issue collect surveys and contribution articles on emerging trends and technologies in ubiquitous MEC for future generation IoT networks and their managements. The papers related to machine learning, deep learning, optimization, blockchain, 5G, or beyond solutions, especially for domain-specific IoT network management, which use MEC environments, are collected after evaluating the review process. Each paper submitted to this special issue was reviewed by three to seven experts during the assessment process. At the end we consider one survey paper and four research contributions.

The first paper Ravi et al. proposed a survey on “Stochastic modeling and performance analysis in balancing load and traffic for vehicular ad hoc networks.” This survey presents recently published stochastic modeling-based algorithms for VANETs. This article briefly covers various queueing models for the reader's convenience. This paper discusses a variety of VANET issues such as mobility, routing, data dissemination, cooperative communication, congestion control, and traffic load balancing issues addressed by stochastic modeling techniques. The authors