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Information and communication technologies are spreading rapidly due to their fast proliferation in many fields. The number of Internet users has led to a spike in cyber-attack incidents. E-commerce applications, such as online banking, marketing, trading, and other online businesses, play an integral role in our lives. Network Intrusion Detection System (NIDS) is essential to protect the network from unauthorized access and against other cyber-attacks. The existing NIDS systems are based on the Backward Oracle Matching (BOM) algorithm, which minimizes the false alarm rate and causes of high packet drop ratio. This paper discussed the existing NIDS systems and different used pattern-matching techniques regarding their weaknesses and limitations. To address the existing system issues, this paper proposes an enhanced version of the BOM algorithm by using multiple pattern-matching methods for the NIDS system to improve the network performance. The proposed solution is tested in simulation with existing solutions using the Snort and NSL-KDD datasets. The experimental results indicated that the proposed solution performed better than the existing solutions and achieved a 5.17% detection rate and a 0.22% lower false alarm rate than the existing solution. [ABSTRACT FROM AUTHOR]

This study describes improving network security by implementing and assessing an intrusion detection system (IDS) based on deep neural networks (DNNs). The paper investigates contemporary technical ways for enhancing intrusion detection performance, given the vital relevance of safeguarding computer networks against harmful activity. The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset, a popular benchmark for IDS research. The model performs well in both the training and validation stages, with 91.30% training accuracy and 94.38% validation accuracy. Thus, the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation. Furthermore, for both macro and micro averages across class 0 (normal) and class 1 (anomalous) data, the study evaluates the model using a variety of assessment measures, such as accuracy scores, precision, recall, and F1 scores. The macro-average recall is 0.9422, the macro-average precision is 0.9482, and the accuracy scores are 0.942. Furthermore, macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model's ability to precisely identify anomalies precisely. The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved by DNN-based intrusion detection systems, which can significantly improve network security. The study underscores the critical function of DNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field. Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge. [ABSTRACT FROM AUTHOR]

To improve the tracking accuracy of persons in the surveillance video, we proposed an algorithm for multi-target tracking persons based on deep learning. In this paper, we used You Only Look Once v5 (YOLOv5) to obtain person targets of each frame in the video and used Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) to do cascade matching and Intersection Over Union (IOU) matching of person targets between different frames. To solve the IDSwitch problem caused by the low feature extraction ability of the Re-Identification (ReID) network in the process of cascade matching, we introduced Spatial Relation-aware Global Attention (RGA-S) and Channel Relation-aware Global Attention (RGA-C) attention mechanisms into the network structure. The pre-training weights are loaded for Transfer Learning training on the dataset CUHK03. To enhance the discrimination performance of the network, we proposed a new loss function design method, which introduces the Hard-Negative-Mining way into the benchmark triplet loss. To improve the classification accuracy of the network, we introduced a Label-Smoothing regularization method to the cross-entropy loss. To facilitate the model's convergence stability and convergence speed at the early training stage and to prevent the model from oscillating around the global optimum due to excessive learning rate at the later stage of training, this paper proposed a learning rate regulation method combining Linear-Warmup and exponential decay. The experimental results on CUHK03 show that the mean Average Precision (mAP) of the improved ReID network is 76.5%. The Top 1 is 42.5%, the Top 5 is 65.4%, and the Top 10 is 74.3% in Cumulative Matching Characteristics (CMC); Compared with the original algorithm, the tracking accuracy of the optimized DeepSORT tracking algorithm is improved by 2.5%, the tracking precision is improved by 3.8%. The number of identity switching is reduced by 25%. The algorithm effectively alleviates the IDSwitch problem, improves the tracking accuracy of persons, and has a high practical value. [ABSTRACT FROM AUTHOR]

Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity, and because of its complexity and evasiveness, it is challenging to identify using traditional signature-based detection approaches. The study article discusses the growing danger to cyber security that malware hidden in PDF files poses, highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies. The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree. Using a dataset from the Canadian Institute for Cybersecurity, a comparative analysis is carried out with well-known machine learning models, such as Credal Decision Tree, Naïve Bayes, Average One Dependency Estimator, Locally Weighted Learning, and Stochastic Gradient Descent. Beyond traditional structural and Java Scriptcentric PDF analysis, the research makes a substantial contribution to the area by boosting precision and resilience in malware detection. The use of Logistic Model Tree, a thorough feature selection approach, and increased focus on PDF file attributes all contribute to the efficiency of PDF virus detection. The paper emphasizes Logistic Model Tree's critical role in tackling increasing cybersecurity threats and proposes a viable answer to practical issues in the sector. The results reveal that the Logistic Model Tree is superior, with improved accuracy of 97.46% when compared to benchmark models, demonstrating its usefulness in addressing the ever-changing threat landscape. [ABSTRACT FROM AUTHOR]

In various fields, different networks are used, most of the time not of a single kind; but rather a mix of at least two networks. These kinds of networks are called bridge networks which are utilized in interconnection networks of PC, portable networks, spine of internet, networks engaged with advanced mechanics, power generation interconnection, bio-informatics and substance intensify structures. Any number that can be entirely calculated by a graph is called graph invariants. Countless mathematical graph invariants have been portrayed and utilized for connection investigation during the latest twenty years. Nevertheless, no trustworthy evaluation has been embraced to pick, how much these invariants are associated with a network graph or subatomic graph. In this paper, it will discuss three unmistakable varieties of bridge networks with an incredible capacity of assumption in the field of computer science, chemistry, physics, drug industry, informatics and arithmetic in setting with physical and manufactured developments and networks, since Contraharmonic-quadratic invariants (CQIs) are recently presented and have different figure qualities for different varieties of bridge graphs or networks. The study settled the geography of bridge graphs/networks of three novel sorts with two kinds of CQI and Quadratic-Contraharmonic Indices (QCIs). The deduced results can be used for the modeling of the above-mentioned networks. [ABSTRACT FROM AUTHOR]

Distributed denial-of-service (DDoS) attacks are designed to interrupt network services such as email servers and webpages in traditional computer networks. Furthermore, the enormous number of connected devices makes it difficult to operate such a network effectively. Software defined networks (SDN) are networks that are managed through a centralized control system, according to researchers. This controller is the brain of any SDN, composing the forwarding table of all data plane network switches. Despite the advantages of SDN controllers, DDoS attacks are easier to perpetrate than on traditional networks. Because the controller is a single point of failure, if it fails, the entire network will fail. This paper offers a Hybrid Deep Learning Intrusion Detection and Prevention (HDLIDP) framework, which blends signature-based and deep learning neural networks to detect and prevent intrusions. This framework improves detection accuracy while addressing all of the aforementioned problems. To validate the framework, experiments are done on both traditional and SDN datasets; the findings demonstrate a significant improvement in classification accuracy. [ABSTRACT FROM AUTHOR]

Any number that can be uniquely determined by a graph is called a graph invariant. During the last twenty years' countless mathematical graph invariants have been characterized and utilized for correlation analysis. However, no reliable examination has been embraced to decide, how much these invariants are related with a network graph or molecular graph. In this paper, it will discuss three different variants of bridge networks with good potential of prediction in the field of computer science, mathematics, chemistry, pharmacy, informatics and biology in context with physical and chemical structures and networks, because k-banhatti sombor invariants are freshly presented and have numerous prediction qualities for different variants of bridge graphs or networks. The study solved the topology of a bridge graph/networks of three different types with two invariants KBanhatti Sombor Indices and its reduced form. These deduced results can be used for the modeling of computer networks like Local area network (LAN), Metropolitan area network (MAN), andWide area network (WAN), backbone of internet and other networks/structures of computers, power generation, bio-informatics and chemical compounds synthesis. [ABSTRACT FROM AUTHOR]

The Floyd-Warshall algorithm is frequently used to determine the shortest path between any pair of nodes. It works well for crisp weights, but the problem arises when weights are vague and uncertain. Let us take an example of computer networks, where the chosen path might no longer be appropriate due to rapid changes in network conditions. The optimal path from among all possible courses is chosen in computer networks based on a variety of parameters. In this paper, we design a new variant of the Floyd-Warshall algorithm that identifies an All-Pair Shortest Path (APSP) in an uncertain situation of a network. In the proposed methodology, multiple criteria and their mutual association may involve the selection of any suitable path between any two node points, and the values of these criteria may change due to an uncertain environment. We use trapezoidal picture fuzzy addition, score, and accuracy functions to find APSP. We compute the time complexity of this algorithm and contrast it with the traditional Floyd-Warshall algorithm and fuzzy Floyd-Warshall algorithm. [ABSTRACT FROM AUTHOR]

In this current century, most industries are moving towards automation, where human intervention is dramatically reduced. This revolution leads to industrial revolution 4.0, which uses the Internet of Things (IoT) and wireless sensor networks (WSN). With its associated applications, this IoT device is used to compute the receivedWSN data from devices and transfer it to remote locations for assistance. In general, WSNs, the gateways are a long distance from the base station (BS) and are communicated through the gateways nearer to the BS. At the gateway, which is closer to the BS, energy drains faster because of the heavy load, which leads to energy issues around the BS. Since the sensors are battery-operated, either replacement or recharging of those sensor node batteries is not possible after it is deployed to their corresponding areas. In that situation, energy plays a vital role in sensor survival. Concerning reducing the network energy consumption and increasing the network lifetime, this paper proposed an efficient cluster head selection using Improved Social spider Optimization with a Rough Set (ISSRS) and routing path selection to reduce the network load using the Improved Grey wolf optimization (IGWO) approach. (i) Using ISSRS, the initial clusters are formed with the local nodes, and the cluster head is chosen. (ii) Load balancing through routing path selection using IGWO. The simulation results prove that the proposed optimization-based approaches efficiently reduce the energy through load balancing compared to existing systems in terms of energy efficiency, packet delivery ratio, network throughput, and packet loss percentage. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT), the users have complex needs, and the Web Service Composition (WSC) was introduced to address these needs. The WSC's main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services (QoS) constraints. The challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS constraints. In this paper, we introduce an extension of our previous works on the Artificial Bee Colony (ABC) and Bat Algorithm (BA). A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global search. The bat agent is used to improve the solution of exhausted bees after a threshold (limits), and also an Elitist Strategy (ES) is added to BA to increase the convergence rate. The performance and convergence behavior of the proposed hybrid algorithm was tested using extensive comparative experiments with current state-of-the-art nature-inspired algorithms on 12 benchmark datasets using three evaluation criteria (average fitness values, best fitness values, and execution time) that were measured for 30 different runs. These datasets are created from real-world datasets and artificially to form different scale sizes of WSC datasets. The results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to competitors. TheWilcoxon signed-rank significant test is usedwhere the proposed algorithm results significantly differ fromother algorithms on 100% of datasets. [ABSTRACT FROM AUTHOR]

Interconnected devices and intelligent applications have slashed human intervention in the Internet of Things (IoT), making it possible to accomplish tasks with less human interaction. However, it faces many problems, including lower capacity links, energy utilization, enhancement of resources and limited resources due to its openness, heterogeneity, limited resources and extensiveness. It is challenging to route packets in such a constrained environment. In an IoT network constrained by limited resources, minimal routing control overhead is required without packet loss. Such constrained environments can be improved through the optimal routing protocol. It is challenging to route packets in such a constrained environment. Thus, this work is motivated to present an efficient routing protocol for enhancing the lifetime of the IoT network. Lightweight On-demand Ad hoc Distance-vector Routing Protocol--Next Generation (LOADng) protocol is an extended version of the Ad Hoc On-Demand Distance Vector (AODV) protocol. Unlike AODV, LOADng is a lighter version that forbids the intermediate nodes on the route to send a route reply (RREP) for the route request (RREQ), which originated from the source. A resource-constrained IoT network demands minimal routing control overhead and faster packet delivery. So, in this paper, the parameters of the LOADng routing protocol are optimized using the black widow optimization (BWO) algorithm to reduce the control overhead and delay. Furthermore, the performance of the proposed model is analyzed with the default LOADng in terms of delay, delivery ratio and overhead. Obtained results show that the LOADng-BWO protocol outperforms the conventional LOADng protocol. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) environment plays a crucial role in the design of smart environments. Security and privacy are the major challenging problems that exist in the design of IoT-enabled real-time environments. Security susceptibilities in IoT-based systems pose security threats which affect smart environment applications. Intrusion detection systems (IDS) can be used for IoT environments to mitigate IoT-related security attacks which use few security vulnerabilities. This paper introduces a modified garden balsan optimizationbased machine learning model for intrusion detection (MGBO-MLID) in the IoT cloud environment. The presented MGBO-MLID technique focuses on the identification and classification of intrusions in the IoT cloud atmosphere. Initially, the presented MGBO-MLID model applies min-max normalization that can be utilized for scaling the features in a uniform format. In addition, the MGBO-MLID model exploits the MGBO algorithm to choose the optimal subset of features. Moreover, the attention-based bidirectional long short-term (ABiLSTM) method can be utilized for the detection and classification of intrusions. At the final level, the Aquila optimization (AO) algorithm is applied as a hyperparameter optimizer to fine-tune the ABiLSTM methods. The experimental validation of the MGBO-MLID method is tested using a benchmark dataset. The extensive comparative study reported the betterment of the MGBO-MLID algorithm over recent approaches. [ABSTRACT FROM AUTHOR]

The internet has become a part of every human life. Also, various devices that are connected through the internet are increasing. Nowadays, the Industrial Internet of things (IIoT) is an evolutionary technology interconnecting various industries in digital platforms to facilitate their development. Moreover, IIoT is being used in various industrial fields such as logistics, manufacturing, metals and mining, gas and oil, transportation, aviation, and energy utilities. It is mandatory that various industrial fields require highly reliable security and preventive measures against cyber-attacks. Intrusion detection is defined as the detection in the network of security threats targeting privacy information and sensitive data. Intrusion Detection Systems (IDS) have taken an important role in providing security in the field of computer networks. Prevention of intrusion is completely based on the detection functions of the IDS. When an IIoT network expands, it generates a huge volume of data that needs an IDS to detect intrusions and prevent network attacks. Many research works have been done for preventing network attacks. Every day, the challenges and risks associated with intrusion prevention are increasing while their solutions are not properly defined. In this regard, this paper proposes a training process and a wrapper-based feature selection With Direct Linear Discriminant Analysis LDA (WDLDA). The implemented WDLDA results in a rate of detection accuracy (DRA) of 97% and a false positive rate (FPR) of 11% using the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset. [ABSTRACT FROM AUTHOR]

To overcome such shortcomings of keyword-based systems on the large text database as low efficiency and recall of searching, this paper proposes a novel concept semantic space to describe the large scale of text database efficiently. The proposed concept semantic space describes the text database from m ultiple semantic granularities (i.e. keyword, concept, etc.) and m ultiple semantic dimensions (i.e. association relations, similar relations, etc.), which provides a macroscopic and dynamic v iew of the text database. With the support of concept semantic space, some novel systems can be constructed on the text database to provide novel, efficient and flexible services. In this paper, take association relation for example, the main steps of b u ild in g such a concept semantic space on text database are discussed in detail. In the end, both the experimental results and a prototype system, named as Knowle, show that the proposed concept semantic space is efficient in organizing the text database. [ABSTRACT FROM AUTHOR]

Any number that can be uniquely determined by a graph is called graph invariants. During the most recent twenty years' innumerable numerical graph invariants have been described and used for correlation analysis. In the fast and advanced environment of manufacturing of networks and other products which used different networks, no dependable assessment has been embraced to choose, how much these invariants are connected with a network graph or molecular graph. In this paper, it will talk about three distinct variations of bridge networks with great capability of expectation in the field of computer science, chemistry, physics, drug industry, informatics, and mathematics in setting with physical and synthetic constructions and networks, since K-Banhatti invariants are newly introduced and have various forecast characteristics for various variations of bridge graphs or networks. The review settled the topology of bridge graph/networks of three unique sorts with three types of K-Banhatti Indices. These concluded outcomes can be utilized for the modeling of interconnection networks of Personal computers (PC), networks like Local area network (LAN), Metropolitan area network (MAN) and Wide area network (WAN), the spine of internet and different networks/designs of PCs, power generation interconnection, bio-informatics and chemical structures. [ABSTRACT FROM AUTHOR]

In recent years, due to the rapid progress of various technologies, wireless computer networks have developed. However, the activities of the security threats and attackers affect the data communication of these technologies. So, to protect the network against these security threats, an efficient IDS (Intrusion Detection System) is presented in this paper. Namely, optimized long short-term memory (OLSTM) network with a stacked auto-encoder (SAE) network is proposed as an IDS system. Using SAE, significant features are extracted from the databases such as input NSL-KDD database and the UNSW-NB15 database. Then extracted features are given as input to the optimized LSTM which is used as an intrusion identification system. To enhance the effectiveness of the LSTM, we present the pigeon optimization algorithm (POA). Using this algorithm, weight parameters of the LSTM are chosen optimally. Finally, the proposed IDS model decides whether the input packets are intruded or not. The results confirm that the proposed IDS model surpasses the previous machine learning-based IDS models in terms of correctness, F1-score and G mean. [ABSTRACT FROM AUTHOR]

Many applications require to send information from a source node to multiple destinations nodes. To support these applications, the paper presents a multi-objective based genetic algorithm, which is used in the construction of the multicast tree for data transmission in a computer network. The proposed algorithm simultaneously optimizes total weights (cost, delay, and hop) of the multicast tree. Experimental results prove that the proposed algorithm outper-forms a recently published Multi-objective Multicast Algorithm specially designed for solving the multicast routing problem. Also, the proposed approach has been applied to ten-node and twenty-node network to illustrate its efficiency. In addition, the execution time is reported for each studied case and the obtained results are compared with the results obtained by the previously based ant colony algorithm presented recently to solve the same problem. Finality, summing up the three objectives (cost, delay, and hop) to be one objective called the weight of the tree to speed up the searching process by using the proposed algorithm to find the best solutions. [ABSTRACT FROM AUTHOR]

An "Intrusion Detection System" (IDS) is a security measure designed to perceive and be aware of unauthorized access ormalicious activity on a computer system or network. Signature-based IDSs employ an attack signature database to identify intrusions. This indicates that the system can only identify known attacks and cannot identify brand-new or unidentified assaults. In Wireless 6G IoT networks, signature-based IDSs can be useful to detect a wide range of known attacks such as viruses, worms, and Trojans. However, these networks have specific requirements and constraints, such as the need for real-time detection and low-power operation. To meet these requirements, the IDS algorithm should be designed to be efficient in terms of resource usage and should include a mechanism for updating the attack signatures to keep up with evolving threats. This paper provides a solution for a signature-based intrusion detection system in wireless 6G IoT Networks, in which three different algorithms were used and implemented by using python and JavaScript programming languages and an accuracy of 98.9% is achieved. [ABSTRACT FROM AUTHOR]

In the distributed networks, many applications send information from a source node to multiple destination nodes. To support these applications requirements, the paper presents a multi-objective algorithm based on ant colonies to construct a multicast tree for data transmission in a computer network. The proposed algorithm simultaneously optimizes total weight (cost, delay and hop) of the multicast tree. Experimental results prove the proposed algorithm outperforms a recently published Multi-objective Multicast Algorithm specially designed for solving the multicast routing problem. Also, it is able to find a better solution with fast convergence speed and high reliability. [ABSTRACT FROM AUTHOR]

With the development of Information technology and the popularization of Internet, whenever and wherever possible, people can connect to the Internet optionally. Meanwhile, the security of network traffic is threatened by various of online malicious behaviors. The aim of an intrusion detection system (IDS) is to detect the network behaviors which are diverse and malicious. Since a conventional firewall cannot detect most of the malicious behaviors, such as malicious network traffic or computer abuse, some advanced learning methods are introduced and integrated with intrusion detection approaches in order to improve the performance of detection approaches. However, there are very few related studies focusing on both the effective detection for attacks and the representation for malicious behaviors with graph. In this paper, a novel intrusion detection approach IDBFG (Intrusion Detection Based on Feature Graph) is proposed which first filters normal connections with grid partitions, and then records the patterns of various attacks with a novel graph structure, and the behaviors in accordance with the patterns in graph are detected as intrusion behaviors. The experimental results on KDD-Cup 99 dataset show that IDBFG performs better than SVM (Supprot Vector Machines) and Decision Tree which are trained and tested in original feature space in terms of detection rates, false alarm rates and run time. [ABSTRACT FROM AUTHOR]

Cloud computing technology is the culmination of technical advancements in computer networks, hardware and software capabilities that collectively gave rise to computing as a utility. It offers a plethora of utilities to its clients worldwide in a very cost-effective way and this feature is enticing users/companies to migrate their infrastructure to cloud platform. Swayed by its gigantic capacity and easy access clients are uploading replicated data on cloud resulting in an unnecessary crunch of storage in datacenters. Many data compression techniques came to rescue but none could serve the purpose for the capacity as large as a cloud, hence, researches were made to de-duplicate the data and harvest the space from exiting storage capacity which was going in vain due to duplicacy of data. For providing better cloud services through scalable provisioning of resources, interoperability has brought many Cloud Service Providers (CSPs) under one umbrella and termed it as Cloud Federation. Many policies have been devised for private and public cloud deployment models for searching/eradicating replicated copies using hashing techniques. Whereas the exploration for duplicate copies is not restricted to any one type of CSP but to a set of public or private CSPs contributing to the federation. It was found that even in advanced deduplication techniques for federated clouds, due to the different nature of CSPs, a single file is stored at private as well as public group in the same cloud federation which can be handled if an optimized deduplication strategy be rendered for addressing this issue. Therefore, this study has been aimed to further optimize a deduplication strategy for federated cloud environment and suggested a central management agent for the federation. It was perceived that work relevant to this is not existing, hence, in this paper, the concept of federation agent has been implemented and deduplication technique following file level has been used for the accomplishment of this approach. [ABSTRACT FROM AUTHOR]

As the power Internet of Things (IoT) enters the security construction stage, the massive use of perception layer devices urgently requires an identity authentication scheme that considers both security and practicality. The existing public key infrastructure (PKI)-based security authentication scheme is currently difficult to apply in many terminals in IoT. Its key distribution and management costs are high, which hinders the development of power IoT security construction. Combined Public Key (CPK) technology uses a small number of seeds to generate unlimited public keys. It is very suitable for identity authentication in the power Internet of Things. In this paper, we propose a novel identity authentication scheme for power IoT. The scheme combines the physical unclonable function (PUF) with improved CPK technology to achieve mutual identity authentication between power IoT terminals and servers. The proposed scheme does not require third-party authentication and improves the security of identity authentication for power IoT. Moreover, the scheme reduces the resource consumption of power IoT devices. The improved CPK algorithm solves the key collision problem, and the third party only needs to save the private key and the public key matrix. Experimental results show that the amount of storage resources occupied in our scheme is small. The proposed scheme is more suitable for the power IoT. [ABSTRACT FROM AUTHOR]

Cluster as a classical paradigm has been often used in a multitasking mobile network and evolved into a dynamic cluster, which is motivated by the tasks with dynamic scale and nodal locations. An important question is how to synthesize the power of multiply members in a dynamic cluster and enhance the overall performance of the network. The almost universal setting is that most clusters are based on the fully connected network and the same level of tasks. To solve these questions, it should evaluate and develop the cooperation between nodes in a dynamic cluster and further propose a corresponding cooperation model. In this paper, the theoretical analysis and the basic models of cluster and dynamic cluster are introduced at first. Then an improved cooperation model of a dynamic cluster is proposed in multitasking mobile networks, which is named, cooperative dynamic cluster. As the main elements of the proposed model, the network connectivity and task priorities are also considered and discussed. Finally, a cooperative message forwarding mechanism is established to settle the cluster, the scales of which should be bounded at a reasonable level in view of a time cost. Simulation results demonstrate the efficiency of our proposed cooperative model was higher than the general dynamic cluster and normal method without cluster, respectively about 14% and 66% higher. [ABSTRACT FROM PUBLISHER]

Performance modeling forms an essential process for evaluation of cloud quality. Cloud performance appraisal process and methods widely differ from that of other proven performance related methodologies being used for domains like computer networks, distributed computing and operations systems. Multi-tier Cloud is a scalable system in which many services or tiers can be constructed for all types of applications. The quality of service of a Multi-tier cloud environment is closely associated with several factors like dependability, availability, reliability, security, perform-ability and each of the performance entities directly or indirectly influences the overall functioning of the cloud. There are many models to evaluate cloud performance and quality, but these traditional models are not efficient enough and consider only certain primary parameters in their evaluation. In this paper, a high-level performance analysis model is proposed that can predict the availability of a Multi-tier cloud environment. As Multi-tier cloud deals with multiple services according to their application, the prediction of availability is essential one for performance modeling. The various prominent parameters that influence the performance of the cloud system are also considered in the proposed model. The proposed algorithm is experimentally verified by means of SHARPE tool. [ABSTRACT FROM PUBLISHER]

The goal of intrusion detection system (IDS) is to detect various types of malicious against computer networks. Unlike conventional firewall, IDS identifies attacks intelligently using analytical approaches, i.e. data mining and machine learning techniques. Specifically, a current focus of research in machine learning is the combination of multiple classifier systems, which have shown better detection accuracy than single weak classifiers. This paper is dedicated to a comparative study of classifier ensemble methods which have been successfully applied in many applications including intrusion detection task. We conduct a comparative assessment of four popular ensemble methods, i.e. Bagging, Boosting, Voting, and Stacking, based on five base classifiers, i.e. random forest (RF), decision tree (DT), logistic regression (LR), functional trees (FT), and classification & regression tree (CART). Based on our experimental result, it can be revealed that classifier ensemble is a promising method for intrusion detection task. [ABSTRACT FROM AUTHOR]

A new three-layered streaming media network architecture based on the fusion of P2P and CDN which can be used for mobile device users is proposed in this paper. It uses P2P network as the backbone, selects nodes of high-performance, highbandwidth, and stable online time as CDN edge servers, and provides mobile device users with streaming media services under the schedule of the load-balancing servers. Since P2P has the inherent drawback of flow disorder, an optimized idea of P4P has been introduced into P2P network in this architecture to make the flow ordered. Theoretical analysis and experimental results show that this architecture has optimized P2P network with higher transmission efficiency, lower bandwidth utilization of the backbone network and lower cost and fewer resources to provide streaming media services as higher network scalability and quality as possible. A streaming media system for videoon- demand which uses the method proposed in the paper has good performance when a large number of mobile users access concurrently. [ABSTRACT FROM AUTHOR]

In this paper, we study the approximate solutions for some of nonlinear Biomathematics models via the e-epidemic SI1I2R model characterizing the spread of viruses in a computer network and SIR childhood disease model. The reduced differential transforms method (RDTM) is one of the interesting methods for finding the approximate solutions for nonlinear problems. We apply the RDTM to discuss the analytic approximate solutions to the SI1I2R model for the spread of virus HCV-subtype and SIR childhood disease model. We discuss the numerical results at some special values of parameters in the approximate solutions. We use the computer software package such as Mathematical to find more iteration when calculating the approximate solutions. Graphical results and discussed quantitatively are presented to illustrate behavior of the obtained approximate solutions. [ABSTRACT FROM AUTHOR]

Content caching is a core component in Named Data Networking (NDN), where content is cached in routers and served for future requests. However, the adversary can launch verification attack by placing poisoned data into the network with a legitimate name and allow the routers in the delivery path to frequently identify the content. Since NDN employs digital signature on each piece of content, verifying all content will exhaust routers' computational resources due to the massive data in the network. In this paper, we propose a selective verification scheme upon the contents that are hit in the content store and allow the contents that are not actually served to be unverified. We also consider the redundant verification of popular content and incorporated in our design to lessen the re-accessing overhead. Analysis and performance results show that the proposed scheme can greatly mitigate the risk of content verification attacks and save the computational resources of relay nodes. [ABSTRACT FROM AUTHOR]

Undoubtedly, Peer-to-Peer (P2P) technology is an effectively method for delivering streaming media content over the internet. However, current P2P stream systems also suffer many problems, such as huge bandwidth consumption, less effective in heterogeneous environment, etc. Scalable video coding (SVC), as an extension of H.264/AVC standard, has attracted many researchers for its flexible scalability and adaptability to the heterogeneous network. Network coding, an innovation of the field of information theory, is proved to be valuable in P2P application. In this paper, we propose two different network coding schemes (multi-layer network coding and intra-layer network coding), combining with SVC technology to solve the above problems. Simulation result shows system gain significant improvement by employing network coding, especially by intra-layer network coding. [ABSTRACT FROM AUTHOR]

The field of finance heavily relies on cybersecurity to safeguard its systems and clients from harmful software. The identification of malevolent code within financial software is vital for protecting both the financial system and individual clients. Nevertheless, present detection models encounter limitations in their ability to identify malevolent code and its variations, all while encompassing a multitude of parameters. To overcome these obstacles, we introduce a lean model for classifying families of malevolent code, formulated on Ghost-DenseNet-SE. This model integrates the Ghost module, DenseNet, and the squeeze-and-excitation (SE) channel domain attention mechanism. It substitutes the standard convolutional layer in DenseNet with the Ghost module, thereby diminishing the model's size and augmenting recognition speed. Additionally, the channel domain attention mechanism assigns distinctive weights to feature channels, facilitating the extraction of pivotal characteristics of malevolent code and bolstering detection precision. Experimental outcomes on the Malimg dataset indicate that the model attained an accuracy of 99.14% in discerning families of malevolent code, surpassing AlexNet (97.8%) and The visual geometry group network (VGGNet) (96.16%). The proposed model exhibits reduced parameters, leading to decreased model complexity alongside enhanced classification accuracy, rendering it a valuable asset for categorizing malevolent code. [ABSTRACT FROM AUTHOR]

Since its inception, the Internet has been rapidly evolving. With the advancement of science and technology and the explosive growth of the population, the demand for the Internet has been on the rise. Many applications in education, healthcare, entertainment, science, and more are being increasingly deployed based on the internet. Concurrently, malicious threats on the internet are on the rise as well. Distributed Denial of Service (DDoS) attacks are among the most common and dangerous threats on the internet today. The scale and complexity of DDoS attacks are constantly growing. Intrusion Detection Systems (IDS) have been deployed and have demonstrated their effectiveness in defense against those threats. In addition, the research of Machine Learning (ML) and Deep Learning (DL) in IDS has gained effective results and significant attention. However, one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks. These attacks, which are not encountered during the system's training, can lead to misclassification with significant errors. In this research, we focused on addressing the issue of Unknown Attack Detection, combining two methods: Spatial Location Constraint Prototype Loss (SLCPL) and Fuzzy C-Means (FCM). With the proposed method, we achieved promising results compared to traditional methods. The proposed method demonstrates a very high accuracy of up to 99.8% with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset (CICIDS2017) dataset. Particularly, the accuracy is also very high, reaching 99.7%, and the precision goes up to 99.9% for unknown DDoS attacks on the DDoS Evaluation Dataset (CICDDoS2019) dataset. The success of the proposed method is due to the combination of SLCPL, an advanced Open-Set Recognition (OSR) technique, and FCM, a traditional yet highly applicable clustering technique. This has yielded a novel method in the field of unknown attack detection. This further expands the trend of applyingDL andMLtechniques in the development of intrusion detection systems and cybersecurity. Finally, implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks. [ABSTRACT FROM AUTHOR]

Energy-efficient design is becoming important in cognitive radio networks. In this paper, we investigate energy-efficient power allocation problem for multiple-input multiple-output (MIMO) two-way cognitive relay networks. The energy-efficient power allocation is formulated as a non convex optimization problem via maximizing the energy efficiency of the data transmission. The minimum required system data rate, the total power of two cognitive transceiver terminals and cognitive relay node (CRN), and the interference introduced to the PU are jointly considered. By introducing equivalent parametric convex optimization problem of the original problem, an iterative energy-efficient power allocation algorithm is proposed. Simulation results illustrate that the proposed iterative power allocation algorithm achieves higher energy efficiency than traditional capacity maximization method. [ABSTRACT FROM AUTHOR]

Computer networks and power transmission networks are treated as capacitated flow networks. A capacitated flow network may partially fail due to maintenance. Therefore, the capacity of each edge should be optimally assigned to face critical situations--i.e., to keep the network functioning normally in the case of failure at one or more edges. The robust design problem (RDP) in a capacitated flow network is to search for the minimum capacity assignment of each edge such that the network still survived even under the edge's failure. The RDP is known as NP-hard. Thus, capacity assignment problem subject to system reliability and total capacity constraints is studied in this paper. The problem is formulated mathematically, and a genetic algorithm is proposed to determine the optimal solution. The optimal solution found by the proposed algorithm is characterized by maximum reliability and minimum total capacity. Some numerical examples are presented to illustrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

The introduction of new technologies has increased communication network coverage and the number of associating nodes in dynamic communication networks (DCN). As the network has the characteristics like decentralized and dynamic, few nodes in the network may not associate with other nodes. These uncooperative nodes also known as selfish nodes corrupt the performance of the cooperative nodes. Namely, the nodes cause congestion, high delay, security concerns, and resource depletion. This study presents an effective selfish node detection method to address these problems. The Price of Anarchy (PoA) and the Price of Stability (PoS) in Game Theory with the Presence of Nash Equilibrium (NE) are discussed for the Selfish Node Detection. This is a novel experiment to detect selfish nodes in a network using PoA. Moreover, the least response dynamic-based Capacitated Selfish Resource Allocation (CSRA) game is introduced to improve resource usage among the nodes. The suggested strategy is simulated using the Solar Winds simulator, and the simulation results show that, when compared to earlier methods, the new scheme offers promising performance in terms of delivery rate, delay, and throughput. [ABSTRACT FROM AUTHOR]

An Information-Centric Network (ICN) provides a promising paradigm for the upcoming internet architecture, which will struggle with steady growth in data and changes in accessmodels.Various ICN architectures have been designed, including Named Data Networking (NDN), which is designed around content delivery instead of hosts. As data is the central part of the network. Therefore, NDN was developed to get rid of the dependency on IP addresses and provide content effectively. Mobility is one of the major research dimensions for this upcoming internet architecture. Some research has been carried out to solve the mobility issues, but it still has problems like handover delay and packet loss ratio during real-time video streaming in the case of consumer and producer mobility. To solve this issue, an efficient hierarchical Cluster Base Proactive Caching for Device Mobility Management (CB-PC-DMM) in NDN Vehicular Networks (NDN-VN) is proposed, through which the consumer receives the contents proactively after handover during the mobility of the consumer. When a consumer moves to the next destination, a handover interest is sent to the connected router, then the router multicasts the consumer's desired data packet to the next hop of neighboring routers. Thus, once the handover process is completed, consumers can easily get the content to the newly connected router. A CB-PCDMM in NDN-VN is proposed that improves the packet delivery ratio and reduces the handover delay aswell as cluster overhead. Moreover, the intra and inter-domain handover handling procedures in CB-PC-DMM for NDN-VN have been described. For the validation of our proposed scheme, MATLAB simulations are conducted. The simulation results show that our proposed scheme reduces the handover delay and increases the consumer's interest satisfaction ratio. The proposed scheme is compared with the existing stateof-the-art schemes, and the total percentage of handover delays is decreased by up to 0.1632%, 0.3267%, 2.3437%, 2.3255%, and 3.7313% at the mobility speeds of 5 m/s, 10 m/s, 15 m/s, 20 m/s, and 25 m/s, and the efficiency of the packet delivery ratio is improved by up to 1.2048%, 5.0632%, 6.4935%, 6.943%, and 8.4507%. Furthermore, the simulation results of our proposed scheme show better efficiency in terms of Packet Delivery Ratio (PDR) from 0.071 to 0.077 and a decrease in the handover delay from 0.1334 to 0.129. [ABSTRACT FROM AUTHOR]

In order to establish the appraisal target system, the principles and methods of the Fuzzy Analytic Hierarchy Process (FAHP) were described in aspects of structuring the comparative judgment matrix described by interval numbers, solving both the interval weight vector and the comprehensive weights of the general target determined by various layers, and selecting the strengthening proposal out of the various schemes. By an actual project of strengthening stone arch bridge, application of the principles and methods of the FAHP to determine the optimal proposals was introduced. From the paper, it is visual to know how to select the optimal proposal from the various schemes by mathematical principle. It will promote the development of the systemization, standardization and theorization of bridge strengthening. [ABSTRACT FROM AUTHOR]

This writing is an attempt to explain a reliable numerical treatment for stochastic computer virus model. We are comparing the solutions of stochastic and deterministic computer virus models. This paper reveals that a stochastic computer virus paradigm is pragmatic in contrast to the deterministic computer virus model. Outcomes of threshold number C* hold in stochastic computer virus model. If C* < 1 then in such a condition virus controlled in the computer population while C* > 1 shows virus persists in the computer population. Unfortunately, stochastic numerical methods fail to cope with large step sizes of time. The suggested structure of the stochastic non-standard finite difference scheme (SNSFD) maintains all diverse characteristics such as dynamical consistency, boundedness and positivity as defined by Mickens. The numerical treatment for the stochastic computer virus model manifested that increasing the antivirus ability ultimates small virus dominance in a computer community. [ABSTRACT FROM AUTHOR]

In peer-to-peer (P2P) overlay networks, source peers holding multimedia objects can transmit multimedia objects to receiver peers. A multimedia object is realized in a sequence of object units (OUs) which are units of transmission al the P2 P overlay layer. In this paper, we discuss parallel types of multi-source streaming (PMSS) models where a receiver peer can concurrently receive object units of a multimedia object from multiple source peers which can support enough quality of service (QoS). The receiver peer is required to receive object units of the multimedia object with enough QoS, e.g. no object unit loss. Multiple source peers in parallel send object units to a receiver peer. In this paper, each object unit is delivered to the receiver peer so that there is no gap and time to buffer the object unit is reduced. In addition, object units are duplicated by sending parity units of the object units. Here, the receiver peer can deliver every object unit even if some object unit is lost. Since the lost object unit can be recovered from the parity unit and the other object units. We evaluate the PMSS model in terms of loss ratio of object units, buffering time, and gap time between consecutive object units in presence of network fault. [ABSTRACT FROM AUTHOR]

With the continuous increase of the integration densities and complexities, the problem of testing integrated circuits has become much more acute and needs an economic solution with reliable performance. This paper presents the design of a TESTCHIP implementing a multiple polynomial, multiple seed based mixed-mode test technique. Fault simulation experiments on benchmark circuits show that the TESTCHIP is capable of detecting 100% of the faults using a much lower number of test vectors than in the approaches attempted by the other researchers. It also offers lower data storage requirements than that of conventional ATE. The TESTCHIP is capable of testing combinational circuits as well as sequential circuits with scan-path facilities. [ABSTRACT FROM AUTHOR]

Many of today's distributed computing systems in the field do not support the migration of execution entities among computing nodes (luring runtime. The relatively static association between units of processing and computing nodes makes it difficult to implement fault-tolerant behavior or load-balancing schemes. The concept of code migration may provide a solution to the above-mentioned problems. It can be defined as the movement of processes, objects, or components from one computing node to another during system runtime in a distributed environment. With the advent of the virtual machine-based .NET framework, many of the cross-language heterogeneity issues have been resolved. With the commercial implementation, the shared source "Rotor", and the open-source "Mono" implementation on hand, we have focused on cross-operating system heterogeneity issues and present interoperability and migration schemes for applications distributed over different operating systems (namely Linux and Windows 2000) as well as various .NET implementations. Within this paper, we describe the integration of a migration facility with the help of Aspect-Oriented Programming (AOP) into the .NET framework. AOP is interesting as it addresses non-functional system properties on the middleware level, without the need to manipulate lower system layers like the operating system itself. Most features required to implement object or process migration (such as reflection mechanisms or a machine-independent executable format) are already present in the .NET frameworks, so the integration of such a concept is a natural extension of the system capabilities. We have implemented several proof-of-concept applications for different use case scenarios. The paper contains an experimental evaluation of the performance impact of object migration in context of those applications. [ABSTRACT FROM AUTHOR]

This paper presents experience in application of multi-agent technology to design and implementation of multi-agent systems (MASs) intended to cooperatively solve the currently critical tasks in the area of computer network security assurance. These MASs are Agent-based Simulator of Attacks against Computer Networks, Multi-agent Intrusion Detection System and Multi-agent Intrusion Detection Learning System. Each of these MASs is based on strict formal frameworks proposed by authors and designed and implemented as software prototypes on the basis of common agent technology supported by software tool "Multi-agent System Development Kit" developed with participation of the authors. The paper sketches the above MASs and analyses advantages of applying multi-agent technologies for computer network security. [ABSTRACT FROM AUTHOR]

With the improvement of current online communication schemes, it is now possible to successfully distribute and transport secured digital Content via the communication channel at a faster transmission rate. Traditional steganography and cryptography concepts are used to achieve the goal of concealing secret Content on a media and encrypting it before transmission. Both of the techniques mentioned above aid in the confidentiality of feature content. The proposed approach concerns secret content embodiment in selected pixels on digital image layers such as Red, Green, and Blue. The private Content originated from a medical client and was forwarded to a medical practitioner on the server end through the internet. The K-Means clustering principle uses the contouring approach to frame the pixel clusters on the image layers. The content embodiment procedure is performed on the selected pixel groups of all layers of the image using the Least Significant Bit (LSB) substitution technique to build the secret Content embedded image known as the stego image, which is subsequently transmitted across the internet medium to the server end. The experimental results are computed using the inputs from "Open-Access Medical Image Repositories (aylward.org)" and demonstrate the scheme's impudence as the Content concealing procedure progresses. [ABSTRACT FROM AUTHOR]

Lately, the Internet of Things (IoT) application requires millions of structured and unstructured data since it has numerous problems, such as data organization, production, and capturing. To address these shortcomings, big data analytics is the most superior technology that has to be adapted. Even though big data and IoT could make human life more convenient, those benefits come at the expense of security. To manage these kinds of threats, the intrusion detection system has been extensively applied to identify malicious network traffic, particularly once the preventive technique fails at the level of endpoint IoT devices. As cyberattacks targeting IoT have gradually become stealthy and more sophisticated, intrusion detection systems (IDS) must continually emerge to manage evolving security threats. This study devises Big Data Analytics with the Internet of Things Assisted Intrusion Detection using Modified Buffalo Optimization Algorithm with Deep Learning (IDMBOA-DL) algorithm. In the presented IDMBOA-DL model, the Hadoop MapReduce tool is exploited for managing big data. The MBOA algorithm is applied to derive an optimal subset of features from picking an optimum set of feature subsets. Finally, the sine cosine algorithm (SCA) with convolutional autoencoder (CAE) mechanism is utilized to recognize and classify the intrusions in the IoT network. A wide range of simulations was conducted to demonstrate the enhanced results of the IDMBOA-DL algorithm. The comparison outcomes emphasized the better performance of the IDMBOA-DL model over other approaches. [ABSTRACT FROM AUTHOR]

A sensor graph network is a sensor network model organized according to graph network structure. Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks. In sensor networks, network structure recognition is the basis for accurate identification and effective prediction and control of node states. Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks, based on the characteristics of sensor networks, a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal transmission path of the core node. This method which builds on unit patulousness and core node signal propagation (called p-law) can rapidly and effectively achieve the global structure identification of a sensor graph network. Different from the traditional graph network structure recognition algorithms such as modularity maximization and spectral clustering, the proposed method reveals the natural evolution process and law of graph network subgroup generation. Experimental results confirm the effectiveness, accuracy and rationality of the proposed method and suggest that our method can be a new approach for graph network global structure recognition. [ABSTRACT FROM AUTHOR]

Complex networks have been a prominent topic of research for several years, spanning a wide range of fields from mathematics to computer science and also to social and biological sciences. The eigenvalues of the Seidel matrix, Seidel Signless Laplacian matrix, Seidel energy, Seidel Signless Laplacian energy, Maximum and Minimum energy, Degree Sum energy and Distance Degree energy of the Unitary Cayley graphs [UCG] have been calculated. Low-power devices must be able to transfer data across long distances with low delay and reliability. To overcome this drawback a small-world network depending on the unitary Cayley graph is proposed to decrease the delay and increase the reliability and is also used to create and analyze network communication. Small-world networks based on the Cayley graph have a basic construction and are highly adaptable. The simulation result shows that the small-world network based on unitary Cayley graphs has a shorter delay and is more reliable. Furthermore, the maximum delay is lowered by 40%. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of applications. In spite of this, it is challenging to design energy-efficient WSN. The routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of network. In order to solve the restricted energy problem, it is essential to reduce the energy utilization of data, transmitted from the routing protocol and improve network development. In this background, the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-hop Routing Protocol (DEAOA-MHRP) for WSN. The aim of the proposed DEAOA-MHRP model is select the optimal routes to reach the destination in WSN. To accomplish this, DEAOA-MHRP model initially integrates the concepts of Different Evolution (DE) and Arithmetic Optimization Algorithms (AOA) to improve convergence rate and solution quality. Besides, the inclusion of DE in traditional AOA helps in overcoming local optima problems. In addition, the proposed DEAOA-MRP technique derives a fitness function comprising two input variables such as residual energy and distance. In order to ensure the energy efficient performance of DEAOA-MHRP model, a detailed comparative study was conducted and the results established its superior performance over recent approaches. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station (BS). Therefore, energy efficiency and load balancing are very essential in WSN. In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. The selection of Cluster Heads (CHs) and routing path of every CH from the base station is enhanced by the proposed method. It provides the best routing path and increases the lifetime and energy efficiency of the network. End-to-end delay and packet loss rate have also been improved. The proposed GW-IPSO-TS method enhances the evaluation of alive nodes, dead nodes, network survival index, convergence rate, and standard deviation of sensor nodes. Compared to the existing algorithms, the proposed method outperforms better and improves the lifetime of the network. [ABSTRACT FROM AUTHOR]

A computer network can be defined as many computing devices connected via a communication medium like the internet. Computer network development has proposed how humans and devices communicate today. These networks have improved, facilitated, and made conventional forms of communication easier. However, it has also led to uptick in-network threats and assaults. In 2022, the global market for information technology is expected to reach $170.4 billion. However, in contrast, 95% of cyber security threats globally are caused by human action. These networks may be utilized in several control systems, such as home-automation, chemical and physical assault detection, intrusion detection, and environmental monitoring. The proposed literature review presents a wide range of information on Wireless Social Networks (WSNs) and Internet of Things (IoT) frameworks. The aim is first to be aware of the existing issues (issues with traditional methods) and network attacks on WSN and IoT systems and how to defend them. The second is to review the novel work in the domain and find its limitations. The goal is to identify the area’s primary gray field or current research divide to enable others to address the range. Finally, we concluded that configuration. Message Rapid Spanning Tree Protocol (RSTP) messages have higher efficiency in network performance degradation than alternative Bridge Data Unit Protocol (BPDU) forms. The research divides our future research into solutions and newly developed techniques that can assist in completing the lacking component. In this research, we have selected articles from 2015 to 2021 to provide users with a comprehensive literature overview. [ABSTRACT FROM AUTHOR]

The reconfigurable networks in the Chinese national basic research project- Flexible Architecture of Reconfigurable Infrastructure can adjust its own structure to provide better services for applications according to their transactional attributes and is compatible with today’s networks. Data packet authentication is necessary to guarantee that precious resources in the reconfigurable networks couldn’t be abused. In this paper, we propose a novel fast data packet authentication scheme for the reconfigurable networks based on the attribute based signature and RSA accumulator. In our scheme, packets are organized and authenticated in groups. Every group only needs one signature to guarantee the authenticity of the first packet, and other packets in the group can be verified using the membership witness of RSA accumulator embedded in the packet. The proposed scheme can greatly reduce the generation and verification time of the packet authentication information. Simulation results demonstrate the practicality and efficiency of the proposed scheme. [ABSTRACT FROM PUBLISHER]