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There is a growing realization on the part of lawmakers, policy experts and practitioners that cyber risks and threat vectors constitute a critical and strategic challenge for all outer space operations. Stakeholders' professional attention and resources are beginning to focus on measures for mitigating these risks and threats. Space-based activities depend on the reliable functionality of computer systems and the wireless communications connecting them through use of the electromagnetic spectrum resource – all of which constitute parts of the infrastructure of cyberspace both on Earth and in space. Thus, cyber risks and vulnerabilities arise in all aspects of outer space operations: satellite launches and their orbital telemetry, tracking, and control (TTC); operational communications (space-to-space, earth-to-space and space-to-earth); and other spectrum-dependent space transmissions. Additional cyber vulnerabilities are present in the terrestrial systems supporting space operations, including launch systems and supply chains for dedicated equipment and services. The mitigation of such cyber threats and vulnerabilities in the more traditional terrestrial context is a rapidly developing area of law and policy aiming to counter the activities of malicious cyber actors whether they are criminals, States or hostile ideological groups. On the one hand, the threat mitigation aspects of cybersecurity have by no means been resolved, as hostile actors use increasingly sophisticated means to achieve their ends. On the other, progress has been made with respect to measures and best practices recognized as critical to boosting cybersecurity for space activities. This article focuses on one of these: information sharing (IS) among trusted participants in a secure platform designed for sharing actionable cybersecurity data. The primary goal of such sharing is to reduce the informational asymmetry between the attacker and the target, whether the latter is a State, private company, or other entity. Cyber attackers leverage their knowledge of the target's vulnerabilities, and only need to be right on one occasion to cause operational, financial, and/or reputational damage that may impair the long-term functionality of the targeted entity. Yet when a group of potential targets can reliably share data on cyber risks and measures to counter them, the informational asymmetry is reduced, and cybersecurity improved. Several conditions exist for optimizing IS which provides actionable information, and for ensuring that trust is developed and maintained among IS participants. Finally, in the context of outer space-related cybersecurity threats, the paper argues that IS proves critical because of two elements: the aggravated asymmetry of cyber threat vectors in outer space, and their distinctive space-related characteristics. • There is a growing realization that cyber risks and threat vectors constitute a critical and strategic challenge for all outer space operations. • Stakeholders' professional attention and resources are focusing on measures for mitigating these risks and threats. • Space-based activities depend on the reliability of computer systems and their wireless connection, – which constitute the infrastructure of cyberspaceon Earth and in space. • Cyber risks and vulnerabilities arise in all aspects of outer space operations, andadditional. • Cyber vulnerabilities are present in the terrestrial systems supporting them. • Several conditions exist for optimizing IS which is actionable, and for ensuring that trust is maintained among IS participants. • Several conditions exist for optimizing IS which is actionable, and for ensuring that trust is maintained among IS participants. • Information sharing for outer space cybersecurity is critical because of the aggravated asymmetry of cyber threat vectors in space and their distinctive space-related characteristics. [ABSTRACT FROM AUTHOR]

Computers and other digital communication media have replaced paper and pencil from the writer's desk. This development has confronted special collections with a problem, as digital papers are difficult to process using established digital preservation strategies, because of their individual and unique nature. According to the proposal suggested in this paper, the creators should instead be involved in the preservation process, and special collections should integrate pre-custodial forms of curation within their range of tasks. The article outlines the use of a cloud architecture as a suitable instrument for accomplishing this task. The benefits and the prospects for such a collection cloud are exposed and discussed. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology and network communication technology, embedded micro control system has been widely used. It is crucial in the fields of large-scale chemical equipment, mechanized production base, transportation and future robots. Combined with the current Internet of Things technology, the equipment needs to be kept on the network during operation, real-time data comparison, data feedback, data reception and other network operations to provide strong help for human society. Focusing on the network communication level, due to the improvement of hardware and software technology, the basic link work has become the foundation part of realizing network communication, and the real problem is often the information transmission. For example, for wireless channel estimation and optimization based on embedded system, it is necessary to eliminate the interference of irresistible factors as far as possible, overview the defects in algorithm level and logic analysis level since the development of network communication technology, discuss the impact of network communication module of embedded monitoring system in practical application as far as possible, and make the error infinite to zero under the premise of unchanged hardware conditions. Based on the above conditions, the lightweight channel estimation method and the optimization under the condition of sparse matrix are proposed. This paper studies the network communication of embedded devices, especially the channel link in network communication, and focuses on the channel estimation algorithm. This paper proposes the advantages of LS and MMSE algorithms. According to the experimental results, although in terms of speed indicators, it still maintains better than the traditional algorithm in terms of average throughput, transmission delay, response time, transmission rate and so on in a static environment. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we address some computational challenges arising in complex simulation-based design optimization problems. High computational cost, black-box formulation and stochasticity are some of the challenges related to optimization of design problems involving the simulation of complex mathematical models. Solving becomes even more challenging in case of multiple conflicting objectives that must be optimized simultaneously. In such cases, application of multiobjective optimization methods is necessary in order to gain an understanding of which design offers the best possible trade-off. We apply a three-stage solution process to meet the challenges mentioned above. As our case study, we consider the integrated design and control problem in paper mill design where the aim is to decrease the investment cost and enhance the quality of paper on the design level and, at the same time, guarantee the smooth performance of the production system on the operational level. In the first stage of the three-stage solution process, a set of solutions involving different trade-offs is generated with a method suited for computationally expensive multiobjective optimization problems using parallel computing. Then, based on the generated solutions an approximation method is applied to create a computationally inexpensive surrogate problem for the design problem and the surrogate problem is solved in the second stage with an interactive multiobjective optimization method. This stage involves a decision maker and her/his preferences to find the most preferred solution to the surrogate problem. In the third stage, the solution best corresponding that of stage two is found for the original problem. [Copyright &y& Elsevier]

Software defined network (SDN) made with basic concepts that are different from traditional networks in controlling the network, the separation between the control layer and forwarding layer on different devices allows the administrator to adjust the control plan for all devices centralized in one action, while in traditional network, the control and forwarding layers are located in the infrastructure making network administrator must manage devices one by one. Research using single board computers on network technology provides an opportunity to implement SDN architecture. Raspberry Pi has sufficient ability. QoS results meet the ITU-T G.1010 reference which indicates that Raspberry Pi can be used on designed networks. [ABSTRACT FROM AUTHOR]

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, scientific social networks (SSNs) have become the fastest and most convenient way for researchers to communicate with each other. Many published papers are shared via SSNs every day, resulting in the problem of information overload. How to appropriately recommend personalized and highly valuable papers for researchers is becoming more urgent. However, when recommending papers in SSNs, only a small amount of positive instances are available, leaving a vast amount of unlabelled data, in which negative instances and potential unseen positive instances are mixed together, which naturally belongs to One-Class Collaborative Filtering (OCCF) problem. Therefore, considering the extreme data imbalance and data sparsity of this OCCF problem, a hybrid approach of Social and Content aware One-class Recommendation of Papers in SSNs, termed SCORP, is proposed in this study. Unlike previous approaches recommended to address the OCCF problem, social information, which has been proved playing a significant role in performing recommendations in many domains, is applied in both the profiling of content-based filtering and the collaborative filtering to achieve superior recommendations. To verify the effectiveness of the proposed SCORP approach, a real-life dataset from CiteULike was employed. The experimental results demonstrate that the proposed approach is superior to all of the compared approaches, thus providing a more effective method for recommending papers in SSNs. [ABSTRACT FROM AUTHOR]

With the advancement of Industry 4.0, Time-Sensitive Networking (TSN) has become essential for ensuring prompt and reliable data transmission. As an augmentation of Ethernet, TSN aims to supply services capable of low latency, minimal jitter, and low packet loss for urgent data in decentralized, user-oriented networks. Efficient detection techniques are integral to TSN for swiftly determining the practicability of network configurations, as existing schedulability analysis proves insufficient. This paper delves into the potential of backpropagation neural networks (BPNN) in schedulability analysis efficiency. We optimize BPNN using spearman correlation feature selection combined with a voting ensemble method and Particle Swarm Optimization (PSO), forming two models: Spearman-Vote-BPNN and Spearman-PSO-BPNN. Testing on 5,000 network configurations in computer simulations, both models demonstrated high generalization accuracy, around 97.4%. Spearman-Vote-BPNN achieved the fastest training speed at 0.63 s and an accuracy of 98.2%. Meanwhile, Spearman-PSO-BPNN showed the highest accuracy (98.5%) with the quickest detection speed (5.6 ms). The outcomes of this research significantly advance the efficacy and precision of TSN network configuration detection and establish a formidable groundwork for future scholarly pursuits in this area. [ABSTRACT FROM AUTHOR]

The importance and growth of the Internet of Things (IoT) in computer networks and applications have been increasing. Additionally, many of these applications generate large volumes of data, which are critical and require protection against attacks. Various techniques have been proposed to identify and counteract these threats. In this paper, we offer a hybrid machine learning approach (using the k-nearest neighbors and random forests as supervised classifiers) to enhance the accuracy of intrusion detection systems and minimize the risk of potential attacks. Also, we employ backward elimination and linear discriminant analysis algorithms for feature reduction and to lower computational costs. Following the training phase, when discrepancies arose between the decisions of the classifiers, the ultimate determination was supported by ISO/IEC 27001 regulations. The performance of the proposed model was assessed within a Python programming framework, utilizing the CICIDS 2017, NSL-KDD, and TON-IoT datasets. The outcomes illustrated that the proposed approach attained a noteworthy accuracy of 99.96% in the multi-class classification of CICIDS 2017, 99.37% in the binary classification of the NSL-KDD dataset, and 99.96% in the multi-class classification of TON-IoT dataset. Furthermore, the attack success rate for each dataset stands at 0.05%, 0.24%, and 0% respectively, demonstrating a significant reduction compared to other methods. [ABSTRACT FROM AUTHOR]

The chordal ring (CR) graphs are a well-known family of graphs used to model some interconnection networks for computer systems in which all nodes are in a cycle. Generalizing the CR graphs, in this paper, we introduce the families of chordal multi-ring (CMR), chordal ring mixed (CRM), and chordal multi-ring mixed (CMRM) graphs. In the case of mixed graphs, we can have edges (without direction) and arcs (with direction). The chordal ring and chordal ring mixed graphs are bipartite and 3-regular. They consist of a number r (for r ≥ 1 ) of (undirected or directed) cycles with some edges (the chords) joining them. In particular, for CMR, when r = 1 , that is, with only one undirected cycle, we obtain the known families of chordal ring graphs. Here, we used plane tessellations to represent our chordal multi-ring graphs. This allowed us to obtain their maximum number of vertices for every given diameter. Additionally, we computationally obtained their minimum diameter for any value of the number of vertices. Moreover, when seen as a lift graph (also called voltage graph) of a base graph on Abelian groups, we obtained closed formulas for the spectrum, that is, the eigenvalue multi-set of its adjacency matrix. [ABSTRACT FROM AUTHOR]

Traditional vehicular edge computing research usually ignores the mobility of vehicles, the dynamic variability of the vehicular edge environment, the large amount of real‐time data required for vehicular edge computing, the limited resources of edge servers, and collaboration issues. In response to these challenges, this article proposes a vehicular edge computing optimization scheme based on the Lyapunov function and Deep Reinforcement Learning. In this solution, this article uses Digital Twin technology (DT) to simulate the vehicular edge environment. The edge server DT is used to simulate the vehicular edge environment under the edge server, and the base station DT is used to simulate the entire vehicular edge system environment. Based on the real‐time data obtained from DT simulation, this paper defines the Lyapunov function to simplify the migration cost of vehicle tasks between servers into a multi‐objective dynamic optimization problem. It solves the problem by applying the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Experimental results show that compared with other algorithms, this scheme can effectively optimize the allocation and collaboration of vehicular edge computing resources and reduce the delay and energy consumption caused by vehicle task processing. [ABSTRACT FROM AUTHOR]

With large-scale Cyber-Physical Systems (Internet of Things) deployments, the low-cost networked devices are becoming ubiquitous. Time stamping of events is a basic requirement and it is crucial for distributed/isolated systems/devices to work in unison. The system clocks of these devices are not very stable and their behaviour is affected by ageing and environmental conditions. A clock’s timing behaviour is generally analysed independently by comparison to a highly stable standard reference clock using specialized instruments. Network Time Protocol (NTP) is widely used for time synchronization over computer networks. As the NTP server clock is more stable in comparison to a client’s system clock, the variation in the end device’s clock offset values with respect to the NTP server clock over a long time can be considered for analysing the behaviour of the end device’s clock. This paper presents a method for analysing clock behaviour based on NTP messages. A computer programme is developed, which utilizes an open-source NTP client library to exchange NTP messages and records end device’s system clock’s offset variations over time for a given periodicity to analyse the behaviour. This black box analysis approach does not require any direct access to the end device’s clock circuit. The solution may be useful for analysing clock behaviour for long-term stability under actual environmental conditions, for deciding time synchronization periodicity to optimize the number of synchronization packet exchanges, and for pre-deployment timing stability assessment of devices for deployment scenarios having intermittent/non-availability of periodic time synchronization. [ABSTRACT FROM AUTHOR]

Nowadays, different types of computer networks such as Wireless sensor networks (WSNs), the Internet of things (IoT), and wireless body area networks (WBANs) transfer information, share resources, and process information. The IoT is a novel network which interconnects various smart devices and can consist of heterogeneous components such as WSNs for monitoring and collecting information. Characterized by specific advantages, the IoT contains different types of nodes, each with few sensors to collect environmental information on agriculture, ecosystem, search and rescue, conflagrations, etc. Despite extensive applications and high flexibility in the modern world, the IoT faces specific challenges, the most important of which include routing, energy consumption and localization. Localization leads to other network challenges and thus can be considered the most important challenge in the IoT. Localization refers to a process aiming at determining the positions and locations of objects lacking global positioning system (GPS) and needing to use the information of network sensors and topology to estimate their own positions and locations. The distance vector hop (DV-Hop) algorithm is a range-free localization technique, in which the major challenge is that the number of hops between two nodes is multiplied by a number that is the same for all nodes leading to a significant reduction in the localization accuracy. In the method proposed in this paper, a network node with no GPS determines the hops from three anchor nodes with GPS. The location of smart objects can be then estimated according to distances from those anchor nodes. Thereafter, a few positions can be created nearby to mitigate the error. Then each position can be regarded as a member of the grasshopper optimization algorithm (GOA) to minimize the localization error. According to the results obtained from implementation of the proposed algorithm, it is characterized by a lower localization error than grasshopper optimization, butterfly optimization, firefly and swarm optimization algorithms. [ABSTRACT FROM AUTHOR]

Most existing cross-domain recommendation (CDR) systems apply the embedding and mapping idea to tackle the cold-start user problem and, to this end, they learn a common bridge function to transfer the user preferences from the source domain into the target domain. However, sharing a bridge function for all users inevitably leads to biased recommendations. This paper proposes a novel method, named CDR to cold-start users via categorized preference transfer (CDRCPT), to overcome the shortcomings of existing approaches. First, the embeddings of users and items in both the source and target domain are learned through pretraining and we utilize preference encoder to obtain the preference embeddings of users in the source domain. Second, mini-batch clustering is applied in the source domain to group users according to their preferences; here, each cluster identifies a specific class of users, and each cluster is represented by its center. Finally, the general representation is fed into a meta network to learn a bridge function for each available class of users. Experiments on two real data sets show that our CDRCPT method is effective in improving the accuracy and robustness of recommendations. [ABSTRACT FROM AUTHOR]

How can digital covert action be attributed? This paper revisits one of the most complex, most significant, and most mysterious digital covert actions of our time: a 2015 hack-and-leak case known among investigators as 'WikiSaudiLeaks' that so far has evaded attribution. We argue that WikiSaudiLeaks was not a stand-alone event, but a puzzle piece in a larger covert action campaign that involved advanced computer network exploitation, computer network attack, persistent deception, and a creative influence and disinformation effort. By disintegrating the larger event into its components, limited attribution becomes possible. We present the most detailed and comprehensive investigation of this case to date, attribute at least one component of the larger event to Iranian intelligence, and draw conceptional conclusions. [ABSTRACT FROM AUTHOR]

Graph coloring is a fundamental topic in graph theory that requires an assignment of labels (or colors) to vertices or edges subject to various constraints. We focus on the harmonious coloring of a graph, which is a proper vertex coloring such that for every two distinct colors i, j at most one pair of adjacent vertices are colored with i and j. This type of coloring is edge-distinguishing and has potential applications in transportation networks, computer networks, airway network systems. The results presented in this paper fall into two categories: in the first part of the paper we are concerned with the computational aspects of finding a minimum harmonious coloring and in the second part we determine the exact value of the harmonious chromatic number for some particular graphs and classes of graphs. More precisely, in the first part we show that finding a minimum harmonious coloring for arbitrary graphs is APX-hard and that the natural greedy algorithm is a p n)-approximation. In the second part, we determine the exact value of the harmonious chromatic number for all 3-regular planar graphs of diameter 3 and some cycle-related graphs. [ABSTRACT FROM AUTHOR]

With the deepening of industrial technology, the monitoring of the thermodynamic state of complex equipment systems has become a key to ensuring production continuity and safety. The thermodynamic state not only reflects the immediate working performance of the equipment but is also decisive in preventing overheating failures. The application of real-time remote monitoring technology provides a new solution for equipment health management, significantly reducing unexpected downtime, preventing failures, thus improving production efficiency and equipment lifespan. However, existing research still shows limitations in real-time data analysis, diversity of equipment adaptability, and accurate fault prediction. This paper proposes three innovative algorithms for the real-time remote monitoring of the thermodynamic state of complex equipment systems, and a method for mining overheating early warning information. The first algorithm, based on reconstruction error, is suitable for equipment with a large amount of normal operation data, using machine learning technology to accurately simulate the normal state to identify anomalies. The second single-class monitoring algorithm, suitable for situations with only normal operating data, can effectively detect deviations from the normal thermodynamic parameters. The third algorithm, based on statistical quantities, uses the statistical characteristics of equipment operation data for fault warning. In addition, the paper explores the application of the Bucket Sorting Fpgrowth algorithm in the mining of overheating early warning information of the thermodynamic state, analyzing potential fault modes and association rules through efficient data mining technology. These methods not only enhance the applicability and predictive accuracy of monitoring algorithms but also provide valuable decision support for equipment managers. [ABSTRACT FROM AUTHOR]

Cyclic vertex connectivity c κ and cyclic edge connectivity c λ are two important kinds of conditional connectivity, which reflect the number of vertices or edges that can be removed before the graph is disconnected and at least two components contain a cycle, respectively. They have important applications in various networks such as computer networks or biochemical networks. In addition, a fullerene is a special kind of molecule in chemistry. A classic fullerene graph is a 3-connected cubic planar graph with only pentagonal and hexagonal faces. (4, 5, 6)-fullerene graphs are atypical fullerene graphs which also contain 4-faces. In this paper, we prove that c κ = c λ for (4 , 5 , 6) -fullerene graphs except for four exceptional graphs with order less than 16. We also give O (ν) -algorithms to determine the cyclic vertex connectivity and the cyclic edge connectivity of (4 , 5 , 6) -fullerene graphs. [ABSTRACT FROM AUTHOR]

The term "Internet of Things" (IoT) encompasses an entire group of gadgets that are capable of connecting to the Internet in order to gather and share data. The IoT paradigm is being pushed into computer networks by numerous highly advanced intrusions. Cloud computing greatly enhances the success of the IoT by enabling users to perform computing tasks using Internet-based services accessed through connected devices. This seamless integration of cloud technology and the IoT has become a powerful catalyst, revolutionizing the way we operate. The adoption of a distributed architecture, such as cloud computing, exposes the system to potential threats like Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks. To mitigate these risks, the concept of an intrusion detection system (IDS) has been introduced within the cloud environment. Various machine learning (ML) and deep learning (DL) algorithms have been proposed and implemented to effectively detect and respond to such malicious traffic in the cloud system. For dimension reduction during the training process of those algorithms, multiple independent and hybrid techniques have been proposed. This study presents an efficient ML-based real-time IDS framework with proposed hybrid feature selection techniques. Additionally, in this study, a concise comparative analysis has been conducted using five well-known public datasets. The findings presented in this paper reveal that our proposed IDS achieved a maximum accuracy of 99.98% in identifying malicious traffic. [ABSTRACT FROM AUTHOR]

Energy distribution networks represent crucial infrastructures for modern society, and various simulation tools have been widely used by energy suppliers to manage these intricate networks. However, simulation calculations include a large number of fluid control equations, and computational overhead limits the performance of simulation software. This paper proposes a universal parallel simulation framework for energy pipeline networks that takes advantages of data parallelism and computational independence between network elements. A non-pipe model of an energy supply network is optimized, and the input and output of the network model in the proposed framework are modified, which can reduce the development burden during the numerical computations of the pipeline network and weaken the computational correlation between different simulated components. In addition, independent computations can be performed concurrently through periodic data exchange procedures between component instances, improving the parallelism and efficiency of simulation computations. Further, a parallel water pipelines network simulation computing paradigm based on a heterogeneous computer hardware architecture is used to evaluate the proposed framework's performance. A series of tests are conducted to verify the accuracy of the proposed framework, and simulation errors of less than 5% are achieved. The results of multi-threaded simulation experiments have demonstrated the feasibility of the proposed framework in a parallel computing approach. Moreover, an Advanced Micro Devices (AMD) Deep Computing Unit (DCU)-parallel program is implemented into a water supply network simulation system; the computational efficiency of this system is compared with that of its serial counterpart. The experimental results show that the proposed framework is appropriate for high-performance computer architectures, and the 18x speed-up ratio demonstrates that the parallel program based on the proposed universal framework outperforms the serial program. That provides the basis for the application of pipe network simulation on high-performance computers. [ABSTRACT FROM AUTHOR]

Public computer education systems provide students essential opportunities to enhance computer literacy and information skills. However, the widespread adoption of online education technology exposes the field to several critical security risks. Threats, such as malware infections, data breaches, and other network intrusions, are all challenging the security of education systems, posing potential hazards to students' personal information and even the entire teaching environment. To spur further work into specialized anomaly detection techniques for computer education, this paper presents an anomaly detection framework tailored for network services in computer education environments to safeguard these systems. Specifically, the proposed approach learns from large-scale online educational traffic data to classify the security state into five alert levels, enabling more granular anomaly detection and analysis. To assess their detection performance, deep learning and traditional machine learning algorithms are implemented and compared for multi-class intrusion classification. The results show that the proposed framework provides an effective security solution to bolster the integrity and stability of computer education systems against evolving network threats, enhancing threat intelligence to inform proactive security by detecting and characterizing anomalies through multilevel classification. [ABSTRACT FROM AUTHOR]

The Precision Time Protocol (PTP) plays a pivotal role in achieving precise frequency and time synchronization in computer networks. However, network delays and jitter in real systems introduce uncertainties that can compromise synchronization accuracy. Three clock skew estimators designed for the PTP scenario were obtained in our earlier work, complemented by closed-form approximations for the Mean Squared Error (MSE) under the generalized fractional Gaussian noise (gfGn) model, incorporating the Hurst exponent parameter (H) and the a parameter. These expressions offer crucial insights for network designers, aiding in the strategic selection and implementation of clock skew estimators. However, substantial computational resources are required to fit each expression to the gfGn model parameters (H and a) from the MSE perspective requirement. This paper introduces new closed-form estimates that approximate the MSE tailored to match gfGn scenarios that have a lower computational burden compared to the literature-known expressions and that are easily adaptable from the computational burden point of view to different pairs of H and a parameters. Thus, the system requires less substantial computational resources and might be more cost-effective. [ABSTRACT FROM AUTHOR]

In classical broadcast models, information is disseminated in synchronous rounds under the constant communication time model, wherein a node may only inform one of its neighbors in each time-unit—also known as the processor-bound model. These models assume either a coordinating leader or that each node has a set of coordinated actions optimized for each originator, which may require nodes to have sufficient storage, processing power, and the ability to determine the originator. This assumption is not always ideal, and a broadcast model based on the node's local knowledge can sometimes be more effective. Messy models address these issues by eliminating the need for a leader, knowledge of the starting time, and the identity of the originator, relying solely on local knowledge available to each node. This paper investigates the messy broadcast time and optimal scheme in a grid graph, a structure widely used in computer networking systems, particularly in parallel computing, due to its robustness, scalability, fault tolerance, and simplicity. The focus is on scenarios where the originator is located at one of the corner vertices, aiming to understand the efficiency and performance of messy models in such grid structures. [ABSTRACT FROM AUTHOR]

Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats. [ABSTRACT FROM AUTHOR]

This paper proposes a mathematical model to investigate the dynamic behaviors of a modified computer virus model using the Atangana–Baleanu fractional derivative in the Caputo sense, aiming to elucidate the connection between its parameters and network attributes. By introducing a relatively new numerical method, we address the memory-dependent and nonlocal features of the system. The existence and uniqueness of the model’s solution are confirmed. To explore solution trajectories and assess the impact of various input factors on computer virus dynamics, we employ an efficient numerical technique. Our simulations provide insights into the consequences of fractional order, anti-virus measures, asymptotic fraction, damage rate, and removal rate in the system. These findings illuminate the relationships between model parameters, facilitating the design of networks that minimize the risk of virus outbreaks and prevent future cyber threats. By identifying critical factors involved in the progression of viruses, these results enable the development of more effective defense mechanisms. [ABSTRACT FROM AUTHOR]

Log blockchain can be used to ensure the integrity of log data. However, current methods are facing the problems of throughput mismatch and rough audit granularity. Packaging multiple logs to generate integrity proofs improves throughput but reduces audit granularity, and the auditor can only locate the tampering log packet instead of specific records. This paper proposes CFAuditChain, an audit blockchain based on a cuckoo filter, where the proof of existence (PoE) of the single log is calculated while calculating the integrity proof of packet logs. The PoE is saved in the cuckoo filter and stored using blockchain for immutability. Therefore, the auditor can verify the credibility of each log based on the filter when the integrity proof of a log packet does not match. The theoretical analysis and experimental results show that CFAuditChain provides the granularity of audit logs down to the records level at an acceptable cost. [ABSTRACT FROM AUTHOR]

The problem of detecting communities in real-world networks has been extensively studied in the past, but most of the existing approaches work on single-domain networks , i.e. they consider only one type of relationship between nodes. Single-domain networks may contain noisy edges and they may lack some important information. Thus, some authors have proposed to consider the multiple relationships that connect the nodes of a network, thus obtaining multi-domain networks. However, most community detection approaches are limited to multi-layer networks , i.e. networks generated from the superposition of several single-domain networks (called layers) that are regarded as independent of each other. In addition to being computationally expensive, multi-layer approaches might yield inaccurate results because they ignore potential dependencies between layers. This paper proposes a multi-domain discrete-time quantum walks (MDQW) model for multi-domain networks. First, the walking space of network nodes in multi-domain network is constructed. Second, the quantum permutation circuit of the coin state is designed based on the coded particle state. Then, using different coin states, the shift operator performs several quantum walks on the particles. Finally, the corresponding update rule is selected to move the node according to the measurement result of the quantum state. With continuous update iteration, the shift operator automatically optimizes the discovered community structure. We experimentally compared our MDQW method with four state-of-the-art competitors on five real datasets. We used the normalized mutual information (NMI) to compare clustering quality, and we report an increase in NMI of up to 3.51 of our MDQW method in comparison with the second-best performing competitor. The MDQW method is much faster than its competitors, allowing us to conclude that MDQW is a useful tool in the analysis of large real-life multi-domain networks. Finally, we illustrate the usefulness of our approach on two real-world case studies. [ABSTRACT FROM AUTHOR]

Link residual closeness is a newly proposed measure for network vulnerability. In this model, vertices are perfectly reliable and the links fail independently of each other. It measures the vulnerability even when the removal of links does not disconnect the graph. In this paper, we characterize those graphs that maximize the link residual closeness over the connected graphs with fixed order and one additional parameter such as connectivity, edge connectivity, bipartiteness, independence number, matching number, chromatic number, number of cut vertices and number of cut edges. [ABSTRACT FROM AUTHOR]

Multi-dimensional packet classification is one of the most important functions to support various services in next generation routers. Both the memory-efficient data structure to support larger rule tables and the hardware architecture to achieve a higher throughput are desired. In this paper, we propose a parallel and pipelined architecture called Set-Pruning Segment Trees with Buckets (SPSTwB) for multi-dimensional packet classification. SPSTwB significantly reduces rule duplication based on a novel partitioning scheme and an efficient bucket merging scheme. The key feature of our proposed architecture is that memory consumption is reduced significantly regardless of the characteristics of various rule tables. In addition, the logic complexity of each pipeline stage is simplified by not storing rule IDs and priorities and thus it can run at a high clock rate. The proposed scheme needs less than 20 bytes per rule for various 100 K rule tables generated by ClassBench. In addition, the proposed scheme supports fast incremental rule update. The proposed pipelined architecture can achieve a throughput of 134 Gbps from the implementation on Xilinx Virtex-7 FPGA device with dual-ported Block RAM. [ABSTRACT FROM AUTHOR]

This research presents a new problem maximum scatter colored traveling salesman problem (MSCTSP), the objective of MSCTSP is to find Hamiltonian cycles with the minimal edge as max as possible, it is used to simulate the real-world applications of network and transport. Since MSCTSP has been proved to be a NP-hard problem, population-based algorithms can be used for solving it. However, the performances are not satisfactory. Thus, it is necessary to develop novel algorithms to obtain high quality feasible solution. Based on the above reason, the paper proposes a novel hybrid ITÖ (HITÖ) algorithm, which integrates the two new strategies: crossover operator and mutation strategy, into the standard ITÖ. In the iteration course of HITÖ, the dual-chromosome coding is used to code a feasible solution of MSCTSP, and the stochastic drift and volatility operators are used to explore and exploit new unknown region. During the process, drift operator is performed by crossover operator, volatility operator is carried out by mutation strategy, and they are both affected by activity intensity of particles which functionally depends on the radius and temperature. Experiments display HITÖ shows an improvement over comparative algorithms on solution quality. [ABSTRACT FROM AUTHOR]

According to Menger's theorem, connectivity and edge connectivity are closely related to node-disjoint paths and edge-disjoint paths, respectively. Node- and edge-disjoint paths can keep the effective transmission of information and confidentiality. Therefore, node-disjoint paths and edge-disjoint paths are two important parameters to measure the reliability of a network. For a faulty node (resp. edge) set |$S\subset V$| (resp. |$S\subset E$|⁠), a connected graph |$G=(V,E)$| is |$S$| -strongly Menger-node-connected (resp. Menger-edge-connected) if any two distinct nodes |$x$| and |$y$| in |$G-S$| are connected by |$\min \{\deg _{G-S}(x),\deg _{G-S}(y)\}$| internally node-disjoint (resp. edge-disjoint) paths in |$G-S$|⁠ , where |$\deg _{G-S}(x)$| and |$\deg _{G-S}(y)$| are the degrees of |$x$| and |$y$| in |$G-S$|⁠ , respectively. And most of the previous studies are based on networks that are triangle-free. In this paper, we consider the strongly Menger (edge) connectedness of a class of |$r$| -dimensional recursive networks RNCG |$G_{r}$| with triangles. Moreover, we show that |$G_{r}$| is |$(rl-l-1)$| -strongly Menger-node-connected. And then we show that |$G_{r}$| is |$[k+(r-1)l-2]$| -strongly Menger-edge-connected of order 1 and |$[2k+2(r-1)l-6]$| -strongly Menger-edge-connected of order 2. Since the class of |$r$| -dimensional recursive networks RNCG |$G_{r}$| includes not only data center networks DCell and generalized DCell but also interconnection network dragonfly, etc. all the results are appropriate for these networks. [ABSTRACT FROM AUTHOR]

The educational system of the twenty-first century is student-centered. Students are the generation that has seen firsthand the enormous expansion of online media represented by the Internet, virtual reality, and artificial intelligence, which has increased the relevance of digital competence in higher education. This is because they are the ones who have grown up with the rapid development of computer networks and who have firsthand knowledge of those technologies. Additionally, the epidemic has raised public awareness of the need of for digital literacy. The increasing digitalization and modernization of every area of our lives to satisfy the needs of modern education is creating new chances for both teaching and learning. The world over, more and more technology is being used to improve training and education, which eliminates the need to take into account regional factors that are associated with traditional education. More and more educational establishments are using technology to improve instruction. The paper's primary objective is to pinpoint the internet-related and digitalization-related variables that have an impact on higher education in European nations using the following indicators: patent applications, resident population, high-tech exports, research and development spending, and school enrollment at the tertiary level (% gross). Due to data availability, the sample includes 40 nations over the period 1996-2020. The World Bank database is the source of the information. Panel-data VAR and panel Granger causality were the methodologies we used. Our findings suggested that the Internet and digitalization considerably impact higher education because high-technology exports represent both the internet and digitalization. [ABSTRACT FROM AUTHOR]

Modern computer networks' increasing complexity and scale need serious attention towards network anomaly detection. Software‐defined networking (SDN) and graph neural networks (GNN) have emerged as promising approaches for anomaly detection due to their ability to capture dynamic network behavior and learn complex patterns from large‐scale network data. The amalgamation of SDN and GNN for network anomaly detection presents promising opportunities for improving the accuracy and efficiency of network anomaly detection. This paper focuses on various trends, issues, and challenges by integrating GNN on the top of SDN for network anomaly detection. The article highlights the advantages of using SDN for providing fine‐grained control and programmability in network monitoring. At the same time, GNN can model network behavior as a graph and learn representations from graph‐structured data. The authors also discuss the limitations of traditional anomaly detection methods in SDN, such as rule‐based approaches, and the potential of GNN to overcome these limitations by leveraging their ability to capture non‐linear and dynamic patterns in network data. This paper also presents a case study of DoS attack detection using SDN. The result shows that SDN based approach helps to detect attacks with an accuracy of 97% with future research directions. [ABSTRACT FROM AUTHOR]

Introduction/purpose: This paper conducts a comprehensive analysis of a potential implementation of Voice over Internet Protocol (VoIP) systems, focusing on network architecture, VoIP phones, and servers. The study explores potential vulnerabilities and proposes solutions. The paper concludes by advocating for a holistic approach to securing VoIP systems, incorporating supplementary services to ensure the confidentiality, integrity, and availability of voice communications in the digital landscape. Methods: Review of the underlying theory, analysis of the end-user needs and potential solutions, practical viability assesment. Results: The theoretical points discussed were proven in practice, using commercially available resources. Communication was established in an expected manner. Conclusions: Implementing solutions similar to the one presented in the paper would be a relatively inexpensive way to make diverse improvements to the operation of tactical level units, both in peacetime and during war. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes the concept and the technology of social networking federation as a paradigm where information on various social network systems can be seamlessly integrated in order to provide users a uniform and semantic view of their social connections. Such a uniformly fused social network provides a single point of access where all information with respect to one’s social networks can be queried and reasoned about. The goal of the research is to establish a foundation of integrating and assimilating information within multiple social network systems. We designed a reference model of social networking federation system in this paper, as well as some prototype application to demonstrate its paradigm. We believe our work would provide a novel vision of future online social network. [Copyright &y& Elsevier]

Abstract: Objective: To compare how nurses in two different paper-based systems perceive the impact of a computerized physician order entry (CPOE) system on their medication-related activities. Setting: 13 non-surgical, adult inpatient wards in a Dutch academic hospital. Methods: Questionnaire survey of 295 nurses before and 304 nurses after the implementation of a CPOE system. These nurses worked with two different paper-based medication systems before the implementation: ‘Kardex-system’ and ‘TIMED-system’. In the Kardex-system, the structure of the nursing medication work was similar to that of after the CPOE implementation, while in the TIMED-system, it was different. ‘Adaptive Structuration Theory’ (AST) was used to interpret the results. Results: The response rates were 52.2% (154/295) before and 44.7% (136/304) after the implementation. Kardex-nurses reported more positive effects than TIMED-nurses. TIMED-nurses reported that the computerized system was more inflexible, more difficult to work with, and slower than the TIMED-system. In the TIMED group, the overall mean score of the computerized process was not significantly different from that of the paper-based process. Moreover, nurses in both groups were more satisfied with the post-implementation process than with the pre-implementation process. Nevertheless, none of groups reported a better workflow support in the computerized system when compared to that of the paper-based systems. Conclusions: Our findings suggest that not only the technology but also large differences between pre- and post-implementation work structure influence the perceptions of users, and probably make the transition more difficult. This study also suggests that greater satisfaction with a system may not necessarily be a reflection of better workflow support. [Copyright &y& Elsevier]

In this paper, we propose a binary feedback scheme for congestion control in computer networks using a gradient-based mechanism which employs neural network modelling of the system dynamics. The optimal direction for rate adjustment at the source is based on a single bit feedback signal which depends upon the sign of the sensitivity of the system performance index with respect to queue input rate. The paper presents the scheme and the analysis that went into the choice of the various decision mechanisms. Simulation results are presented to show the performance of the gradient based technique as compared to the conventional queue-based approach. Copyright © 2000 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The transmission control protocol (TCP) is one of the most important Internet protocols. It provides reliable transport services between two end-hosts. Since TCP performance affects overall network performance, many studies have been done to model TCP performance in the steady state. However, recent researches have shown that most TCP flows are short-lived. Therefore, it is more meaningful to model TCP performance in relation to the initial stage of short-lived flows. In addition, the next-generation Internet will be an unified all-IP network that includes both wireless and wired networks integrated together. In short, modelling short-lived TCP flows in wireless networks constitutes an important axis of research. In this paper, we propose simple wireless TCP models for short-lived flows that extend the existing analytical model proposed in [IEEE Commun. Lett. 2002; 6(2):85–88]. In terms of wireless TCP, we categorized wireless TCP schemes into three types: end-to-end scheme, split connection scheme, and local retransmission scheme, which is similar to the classification proposed in [IEEE/ACM Trans. Networking 1997; 756–769]. To validate the proposed models, we performed ns-2 simulations. The average differences between the session completion time calculated using the proposed model and the simulation result for three schemes are less than 9, 16, and 7 ms, respectively. Consequently, the proposed model provides a satisfactory means of modelling the TCP performance of short-lived wireless TCP flows. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]