Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

A Self-Attention Based Dynamic Resource Management for Satellite-Terrestrial Networks

The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks,enabling global coverage and offering users ubiquitous computing power support,which is an important development direction of future communications.In this paper,we take into account a multi-scenario network model under the coverage of low earth orbit(LEO)satellite,which can provide computing resources to users in faraway areas to improve task processing efficiency.However,LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex,which makes the extraction of state information a daunting task.Therefore,we explore the dynamic resource management issue pertaining to joint computing,communication resource allocation and power control for multi-access edge computing(MEC).In order to tackle this formidable issue,we undertake the task of transforming the issue into a Markov decision process(MDP)problem and propose the self-attention based dynamic resource management(SABDRM)algorithm,which effectively extracts state information features to enhance the training process.Simulation results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.

Optimizing Resource Management for IoT Devices in Constrained Environments

Embedded computing device implementation on the Internet of Things (IoT) requires careful assessment of their intrinsic resource limitations. These constraints are not limited to memory and processing capabilities but extend to the network interfaces, particularly due to the low-power radio standards that these devices typically employ. The IPv6 protocol is shown to be a strong option for guaranteeing interoperability in the IoT, mostly because of its large address space, the range of current IP-based protocols, and its intrinsic versatility. Considering these benefits, we investigate if current IP-based network management protocols can be implemented on devices with limited resources. We investigate the resource needs in particular for implementing Network Configuration Protocol (NETCONF) and Simple Network Management Protocol (SNMP) on an 8-bit AVR-based device. Our investigation reveals the specific memory and processing demands of these protocols, providing valuable insights into their practicality and efficiency in constrained IoT environments. This study underscores the potential and challenges of leveraging IPv6-based network management protocols to enhance the functionality and interoperability of IoT devices while operating within stringent resource limitations.

Distributionally robust optimization based chance-constrained energy management for hybrid energy powered cellular networks

Energy harvesting has been recognized as a promising technique with which to effectively reduce carbon emis-sions and electricity expenses of base stations.However,renewable energy is inherently stochastic and inter-mittent,imposing formidable challenges on reliably satisfying users'time-varying wireless traffic demands.In addition,the probability distribution of the renewable energy or users’wireless traffic demand is not always fully known in practice.In this paper,we minimize the total energy cost of a hybrid-energy-powered cellular network by jointly optimizing the energy sharing among base stations,the battery charging and discharging rates,and the energy purchased from the grid under the constraint of a limited battery size at each base station.In solving the formulated non-convex chance-constrained stochastic optimization problem,a new ambiguity set is built to characterize the uncertainties in the renewable energy and wireless traffic demands according to interval sets of the mean and covariance.Using this ambiguity set,the original optimization problem is transformed into a more tractable second-order cone programming problem by exploiting the distributionally robust optimization approach.Furthermore,a low-complexity distributionally robust chance-constrained energy management algo-rithm,which requires only interval sets of the mean and covariance of stochastic parameters,is proposed.The results of extensive simulation are presented to demonstrate that the proposed algorithm outperforms existing methods in terms of the computational complexity,energy cost,and reliability.

Ultra Dense Satellite-Enabled 6G Networks:Resource Optimization and Interference Management

With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite networks.This paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks architecture.Then the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are introduced.Satellite mobile edge computing(SMEC)with edge caching in three-layer heterogeneous networks serves to reduce the link traffic of networks.Furthermore,a scheme for interference management is presented,involving quality-of-service(QoS)and co-tier/cross-tier interference constraints.The simulation results show that the proposed schemes can significantly increase the total capacity of ultra dense satellite-enabled 6G heterogeneous networks.

Tax avoidance and earnings management:a neural network approach for the largest European economies

In this study,we investigate the relationship between tax avoidance and earnings management in the largest five European Union economies by using artificial neural network regressions.This methodology allows us to deal with nonlinearities detected in the data,which is the principal contribution to the previous literature.We ana-lyzed Compustat data for Germany,the United Kingdom,France,Italy,and Spain for the 2006–2015 period,focusing on discretionary accruals.We considered three tax avoidance measures,two based on the effective tax rate(ETR)and one on book-tax differences(BTD).Our results indicate the presence of nonlinear patterns and a posi-tive,statistically significant relationship between discretionary accruals and both ETR indicators implying that when companies resort to earnings management,a larger tax-able income—and thus higher ETR and lesser tax avoidance–would ensue.Hence,as also highlighted by the fact that discretionary accruals do not appear to affect BTD,our evidence does not suggest that companies are exploiting tax manipulation to reduce their tax payments;thus,the gap between accounting and taxation seems largely unaf-fected by earnings management.

Lens-Oppositional Wild Geese Optimization Based Clustering Scheme for Wireless Sensor Networks Assists Real Time Disaster Management

Recently,wireless sensor networks(WSNs)find their applicability in several real-time applications such as disaster management,military,surveillance,healthcare,etc.The utilization of WSNs in the disaster monitoring process has gained significant attention among research communities and governments.Real-time monitoring of disaster areas using WSN is a challenging process due to the energy-limited sensor nodes.Therefore,the clustering process can be utilized to improve the energy utilization of the nodes and thereby improve the overall functioning of the network.In this aspect,this study proposes a novel Lens-Oppositional Wild Goose Optimization based Energy Aware Clustering(LOWGO-EAC)scheme for WSN-assisted real-time disaster management.The major intention of the LOWGO-EAC scheme is to perform effective data collection and transmission processes in disaster regions.To achieve this,the LOWGOEAC technique derives a novel LOWGO algorithm by the integration of the lens oppositional-based learning(LOBL)concept with the traditional WGO algorithm to improve the convergence rate.In addition,the LOWGO-EAC technique derives a fitness function involving three input parameters like residual energy(RE),distance to the base station(BS)(DBS),and node degree(ND).The proposed LOWGO-EAC technique can accomplish improved energy efficiency and lifetime of WSNs in real-time disaster management scenarios.The experimental validation of the LOWGO-EAC model is carried out and the comparative study reported the enhanced performance of the LOWGO-EAC model over the recent approaches.

The Management of Mental Health, and Service Networks in Italy

Madness has attracted and frightened for centuries,and talking about this means discussing how this diversity was built and managed in different social contexts and historical periods.Not all societies have had,and still have,the same relationship with madness.It is only with the affirmation of the Modern State,and of Capitalism,that the idea of“normality”indispensable to be able to conceive diversity as something dangerously distant and different from the norm takes over.In our post-modern society,people with mental illness in Italy can resort to specialists and social-health services.But the heterogeneous answers given after the approval of law 180 appear to be increasingly diversified.In this research,much attention will be paid to how the social and health services,located in different areas of Italy(Messina,Rome,Trento)face the current growing risk of social,housing and economic isolation of these fragile subjects.The aim of the research is to explore the possibility of a new relationship between the social-health service and the local community.On the one hand,research investigates what the contribution of the services could be.On the other what the spaces of protagonism and participation of the community could be in inclusion process account.In order to better understand the differences between these two dimensions,a qualitative research approach was chosen through the conduct of in-depth interviews.In this way it was possible to investigate:(1)the partial representations characteristic of the single individual,family members,operators and stackholders in general;(2)the services around the topic dealt with is articulated.From the first results of the research it emerges that the territory can no longer be considered as an abstract entity,but becomes the social space within which the construction of a new community welfare can and must take place.

AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus.The foremost and most prime sector among those affected were schools,colleges,and universities.The education system of entire nations had shifted to online education during this time.Many shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of LMSs.This paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user experience.The AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based chatbots.Session layer enhancements are also required,such as AI-based online proctoring and user authentication using Biometrics.These extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of AI-algorithms.It also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

Design and Implementation of an Open Network Security Management Platform

In order to manage all kinds of network security devices and software systems efficiently, and make them collaborate with each other, the model for an open network security management platform is presented. The feasibility and key implementing technology of the model are expatiated. A prototype system is implemented to validate it.

Aviation-oriented Mobility Management Method in IP/LEO Satellite Networks

Taking into chief consideration the features of aviation nodes in satellite networks, such as high moving speed, long communication distance, and high connection frequency, this article proposes an aviation-oriented mobility management method for IP/low earth orbit （LEO） satellite networks. By introducing the concept of ground station real-time coverage area, the proposed method uses ground-station-based IP addressing method and cell paging scheme to decrease the frequency of IP binding update requests as well as the paging cost. In comparison with the paging mobile IP （P-MIP） method and the handover-independent IP mobility management method, as is verified by the mathematical analysis and simulation, the proposed method could decrease the management cost. It also possesses better ability to support the aviation nodes because it is subjected to fewer influences from increased node speeds and newly coming connection rates.

Light-Weight Simple Network Management Protocol in Wireless Sensor Networks

A Light-Weight Simple Network Management Protocol (LW-SNMP) for the wireless sensor network is proposed, which is a kind of hierarchical network management system including a sink manager, cluster proxies, and node agents. Considering the resource limitations on the sensor nodes, we design new management messages, new data types and new management information base completely. The management messages between the cluster proxy and node agents are delivered as normal data packets. The experiment results show that LW-SNMP can meet the management demands in the resource-limited wireless sensor networks and has a good performance in stability, effectiveness of memory, extensibility than the traditional Simple Network Management Protocol (SNMP).

Cognitive Network Management in Internet of Things

The wide variety of smart embedded computing devices and their increasing number of applications in our daily life have created new op- portunities to acquire knowledge from the physical world anytime and anywhere, which is envisioned as the＂Internet of Things＂ （IoT）. Since a huge number of heterogeneous resources are brought in- to IoT, one of the main challenges is how to effi- ciently manage the increasing complexity of IoT in a scalable, flexNle, and autonomic way. Further- more, the emerging IoT applications will require collaborations among loosely coupled devices, which may reside in various locations of the Inter- net. In this paper, we propose a new IoT network management architecture based on cognitive net- work management technology and Service-Orien- ted Architecture to provide effective and efficient network management of loT.

Quantum Communication Networks and Trust Management: A Survey

This paper summarizes the state of art in quantum communication networks and trust management in recent years.As in the classical networks,trust management is the premise and foundation of quantum secure communication and cannot simply be attributed to security issues,therefore the basic and importance of trust management in quantum communication networks should be taken more seriously.Compared with other theories and techniques in quantum communication,the trust of quantum communication and trust management model in quantum communication network environment is still in its initial stage.In this paper,the core technologies of establishing secure and reliable quantum communication networks are categorized and summarized,and the trends of each direction in trust management of quantum communication network are discussed in depth.

A Trust Management Scheme Based on Behavior Feedback for Opportunistic Networks

In the harsh environment where n ode density is sparse, the slow-moving nodes cannot effectively utilize the encountering opportunities to realize the self-organized identity authentications, and do not have the chance to join the network routing. However, considering m ost of the communications in opportunistic networks are caused by forwarding operations, there is no need to establish the complete mutual authentications for each conversation. Accordingly, a novel trust management scheme is presented based on the information of behavior feedback, in order to complement the insufficiency of identity authentications. By utilizing the certificate chains based on social attributes, the mobile nodes build the local certificate graphs gradually to realize the web of "Identity Trust" relationship. Meanwhile, the successors generate Verified Feedback Packets for each positive behavior, and consequently the "Behavior Trust" relationship is formed for slow-moving nodes. Simulation result shows that, by implementing our trust scheme, the d elivery probability and trust reconstruction ratio can be effectively improved when there are large numbers of compromised nodes, and it means that our trust management scheme can efficiently explore and filter the trust nodes for secure forwarding in opportunistic networks.

Modeling of Intelligent Integrated Network Management System Using Object Oriented Approach *

The concept of intelligent integrated network management (IINM) is briefly introduced. In order to analyze, design and implement IINM successfully, object oriented approach is testified to be an effective and efficient way. In this paper, object oriented technique is applied to the structural model of IINM system, The Domain object class and the MU object class are used to represent the manager and the managed resources. Especially, NM IA is introduced which is a special object class with intelligent behaviors to manage the resources efficiently.

Research and Implementation of Network Intelligent Management Based on CSCW

This paper analyzes progresses and difficulties of subjects on computer network’s management and artificial intelligence, proposes AGIMA, a new model of network intelligent management, which is based on computer supported cooperative work (CSCW) and combining new technologies such as WWW, Java. AGIMA transfers from information distribution centered mode in traditional network management to computing distribution centered mode, providing intelligence capacity for network management by a whole intelligent agent group. The implementation of AGIMA takes much consideration of openess, scalability, proactive adaptability and friendliness of human computer interface. Authors present properties of intelligent agent in details, and conclude that network intelligence should be cooperation between human and computer.

Scalable Load-Adaptive Web-Based Network Management System： An Architectural Prototype

With the advances in enterprise communication and web technologies, web has evolved into a platform for delivering interactive applications. Web-based applications are revolutionizing both the features that can be delivered and the technologies for developing and deploying applications. In this paper, we present a web-based load-adaptive network management system framework. Our system is scalable, extendable, modular and based on open architecture. It is also load-adaptive as it can adapt to the increase in the number of incoming Web Service requests so that the systems performance will not degrade under heavy request loads. To evaluate the proposed architecture, we built a multi-tiered web application testbed with open-source components widely used in industry. Experimental studies conducted on the testbed demonstrated the effectiveness of the proposed approach. By developing the platform and making it available to the research community, we hope to catalyze the development of an open source stack for Web-based network management and monitoring.

A Strategy of Density-Based Partitioning for Scalability Problem in Network Self-management

A density-based partitioning strategy is proposed for large domain networks in order to deal with the scalability issue found in autonomic networks considering, as a scenario, the autonomic Quality of Service （QoS） management context. The approach adopted focus as on obtaining dense network partitions having more paths for a given vertices set in the domain. It is demonstrated that dense partitions improve autonomic processing scalability, for instance, reducing routing process complexity. The solution looks for a significant trade-off between partition autonomic algorithm execution time and path selection quality in large domains. Simulation scenarios for path selection execution time are presented and discussed. Authors argue that autonomic networks may benefit from the dense partition approach proposed by achieving scalable, efficient and near real-time support for autonomic management systems.

What to Cache：Differentiated Caching Resource Allocation and Management in Information-Centric Networking

Information-centric networking(ICN) aims to improve the efficiency of content delivery and reduce the redundancy of data transmission by caching contents in network nodes. An important issue is to design caching methods with better cache hit rate and achieve allocating on-demand. Therefore, an in-network caching scheduling scheme for ICN was designed, distinguishing different kinds of contents and dynamically allocating the cache size on-demand. First discussing what was appropriated to be cached in nodes, and then a classification about the contents could be cached was proposed. Furthermore, we used AHP to weight different contents classes through analyzing users' behavior. And a distributed control process was built, to achieve differentiated caching resource allocation and management. The designed scheme not only avoids the waste of caching resource, but also further enhances the cache availability. Finally, the simulation results are illustrated to show that our method has the superior performance in the aspects of server hit rate and convergence.