International Journal of Network Management最新文献

Swarm of Unmanned Aerial Vehicles (UAVs) broaden the field of application in various fields like military surveillance, crop monitoring in agriculture, combat operations, etc. Unfortunately, they are becoming increasingly susceptible to security attacks, such as jamming, information leakage and spoofing, as they become more common and in more demand. So, there is a wider need for UAVs, which requires the design of strong security procedures to fend off such attacks and security dangers. Even though several studies focused on security aspects, many questions remain unanswered, particularly in the areas of secure UAV-to-UAV communication, support for perfect forward secrecy and non-repudiation. In a battle situation, it is extremely important to close these gaps. The security requirements for the UAV communication protocol in a military setting were the focus of this study. In this paper, we present the issues faced by the UAV swarm, especially during military surveillance operations. To secure the communication link in UAV, a new protocol for UAV Swarm communication is proposed with anonymous secure messaging token-based protocol (ASMTP). The proposed protocol secures UAV-to-base station communication and safeguards the metadata of the sender and receiver nodes. The proposed model maintains the confidentiality, integrity and availability of data in the UAV Swarm and achieves robustness. In addition, it provides a different strategy for the cybersecurity gaps in the swarm of UAVs during military surveillance and combat operations.

The neural network-based technologies have emerged as a potent method for image fusion, object detection, and other computer vision tasks as the rapid development of deep learning. Multi-band infrared images, in particular, capture a more extensive range of radiation details and information compared to conventional single-band infrared images. Consequently, the fusion of multi-band infrared images can provide more features for object detection. However, it is crucial to consider that infrared images may contain sensitive information, potentially leading to privacy concerns. Ensuring datasets privacy protection plays a crucial role in the fusion and tracking process. To address both the need for improved detection performance and the necessity for privacy protection in the infrared environment, we proposed a procedure for object detection based on multi-band infrared image datasets and utilized the transfer learning technique to migrate knowledge learned from external infrared data to internal infrared data, thereby training the infrared image fusion model and detection model. The procedure consists of several steps: (1) data preprocessing of multi-band infrared images, (2) multi-band infrared image fusion, and (3) object detection. Standard evaluation metrics for image fusion and object detection ensure the authenticity of the experiments. The comprehensive validation experiments demonstrate the effectiveness of the proposed procedure in object detection tasks. Furthermore, the transfer learning can train our datasets and update the model without exposing the original data. This aspect of transfer learning is particularly beneficial for maintaining the privacy of multi-band infrared images during the fusion and detection processes.

In view of the randomness of user network usage behavior in data centers, which leads to a large randomness in power load, and considering that a single randomness processing method is usually difficult to fully characterize the uncertain characteristics of the system, this paper proposes a dual fusion prediction analysis model based on an improved error correlation logic regression algorithm and a novel spatiotemporal neural network structure called Cross-TRCN. Two weight coefficients λ1 and λ2 are introduced to fuse the prediction results with different long-term sequence prediction performance, thereby further eliminating the influence of random errors. The results show that it is feasible to predict the workload of data centers based on the improved error correlation logic regression algorithm and the innovative spatiotemporal neural network structure Cross-TRCN.

The difficulties of sending massive amounts of data between several data centres are examined in this work, with particular attention paid to how poorly current scheduling algorithms handle point-to-multipoint transfers and transmission time limits. In this research, a new method called multicast source-based tree (MSBT) is proposed for effectively handling point-to-multipoint transmissions in a certain amount of time. By allowing receivers to simultaneously receive data from several source points, MSBT introduces the idea of ‘source selection’ for the creation of multicast tree structure-based algorithms. Large data blocks are distributed as efficiently as possible using this method, which also guarantees effective transmission from a single-source point to several recipient locations. Furthermore covered in the article is how PV producers and sellers' capacity allocation is affected by the discount rate. These results offer insightful information on how decisions are made in related sectors. The development of new energy big data platforms underscores their significance; leaders in the industry, like United Power, Vision Energy and Goldwind, serve as prime examples.

The adoption and application of mobile communication technology have rapidly escalated, leading to a significant upsurge in the demand of data traffic. Ultra-densification stands as one of the network solutions within the realm of 5G and beyond technologies, aimed to enhance data rates and network capacity. Heterogeneous networks (HetNets) are deployed with different types of small cells (SCs) in mobile networks to provide high capacity, data rate, throughput, and low latency communication. HetNet solves the problem of network densification at the expense of mobility management problems such as ping-pong handover, unnecessary handovers, handover delay, and cell load. This paper introduces an enhanced optimal cell selection technique employing software-defined networking (SDN) to tackle the challenges of handover and mobility management in 5G and beyond 5G (B5G) HetNet. The proposed SDN-based cell selection scheme leverages linear programming (LP) to manage the mobility of users dynamically, facilitating the selection of the optimal cell for user equipment (UE) handover. This selection is based on multi-attribute decision-making criteria, which include user direction, received signal strength (RSS) value, cell load, and dwell time. By applying LP, computational overhead during cell selection is significantly reduced. The results indicate that the proposed scheme leads to a 39% reduction in number of handovers. This reduction signifies a substantial advancement in mitigating issues associated with frequent and unnecessary handovers, ultimately leading to minimized signaling overhead between UE and cells. Moreover, the proposed solution outperformed the existing scheme in terms of system's throughput and selects an optimal target cell with a lower cell load.

Most cryptocurrency spot trading occurs on centralized crypto exchanges, where offers for buying and selling are organized via an order book. In liquid markets, the price achieved for buying and selling deviates only slightly from the assumed reference price, that is, trading is associated with low implicit costs. However, compared to traditional finance, crypto markets are still illiquid, and consequently, the reduction of implicit costs is crucial for any trading strategy and of high interest, especially for institutional investors. This paper describes the design and implementation of Athena, a system that automatically splits orders across multiple exchanges to minimize implicit costs. For this purpose, order books are collected from several centralized crypto exchanges and merged into an internal unified order book. In addition to price and quantity, the entries in the unified order book are enriched with information about the exchange. This enables a smart order routing algorithm to split an order into several slices and execute these on several exchanges to reduce implicit costs and achieve a better price. An extensive evaluation shows the savings of using the smart order routing algorithm.

Synchronization of transaction pools (mempools) has shown potential for improving the performance and block propagation delay of state-of-the-art blockchains. Indeed, various heuristics have been proposed in the literature to incorporate early exchanges of unconfirmed transactions into the block propagation protocol. In this work, we take a different approach, maintaining transaction synchronization externally (and independently) of the block propagation channel. In the process, we formalize the synchronization problem within a graph theoretic framework and introduce a novel algorithm (SREP—set reconciliation-enhanced propagation) with quantifiable guarantees. We analyze the algorithm's performance for various realistic network topologies and show that it converges on static connected graphs in a time bounded by the diameter of the graph. In graphs with dynamic edges, SREP converges in an expected time that is linear in the number of nodes. We confirm our analytical findings through extensive simulations that include comparisons with MempoolSync, a recent approach from the literature. Our simulations show that SREP incurs reasonable bandwidth overhead and scales gracefully with the size of the network (unlike MempoolSync).

To facilitate the management, Internet of Things (IoT) vendors usually apply remote ways such as HTTP services to uniformly manage IoT devices, leading to traditional web application vulnerabilities that also endanger the cloud interfaces of IoT, such as cross-site scripting (XSS), code injection, and Remote Command/Code Execute (RCE). XSS is one of the most common web application attacks, which allows the attacker to obtain private user information or attack IoT devices and IoT cloud platforms. Most of the existing XSS payload detection models are based on machine learning or deep learning, which usually require a lot of external resources, such as pretrained word vectors, to achieve a better performance on unknown samples. But in the field of XSS payload detection, high-quality vector representations of samples are often difficult to obtain. In addition, existing models all perform substantially worse when the distribution of XSS payloads and benign samples in the test dataset is extremely unbalanced (e.g., XSS payloads: benign samples = 1: 20). While in the real XSS attack scenario against IoT, an XSS payload is often hidden in a massive amount of normal user requests, indicating that these models are not practical. In response to the above issues, we propose an XSS payload detection model based on inductive graph neural networks, IGXSS (XSS payload detection model based on inductive GCN), to detect XSS payloads targeting IoT. Firstly, we treat the samples and words obtained from segmenting the samples as nodes and attach lines between them in order to form a graph. Then, we obtain the feature matrix of nodes and edges utilizing information between nodes only (instead of external resources such as pretrained word vectors). Finally, we feed the obtained feature matrix into a two-layer GCN for training and validate the performance of models in several datasets with different sample distributions. Extensive experiments on the real datasets show that IGXSS performs better compared to other models under various sample distributions. In particular, when the sample distribution is extremely unbalanced, the recall and F1 score of IGXSS still reach 1.000 and 0.846, demonstrating that IGXSS is more robust and more suitable for practical scenarios.