The Internet of Vehicles (IoV) is an evolving domain fueled by advancements in vehicular communications and networking. To enhance vehicle coverage, integrating vehicle-to-everything (V2X) networks with cellular networks has become essential, though this integration places increased demand on cellular infrastructure. To address this, we propose a two-tier stable path routing algorithm designed to improve the stability and proactiveness of V2X networks. Our approach divides the coverage area into zones, further segmented into road segments based on road structure. The first tier manages routing within a road segment, while the second tier handles routing between vehicles in adjacent segments. This method improves road awareness, stabilizes topologies, adapts to dynamic changes, and reduces routing overhead. Additionally, the incorporation of Kalman filter-based prediction model further strengthens proactive routing. To validate the proposed approach, we conduct synthetic evaluations across varying vehicular densities with different mobility and traffic scenarios. We compare the traditional centralized routing strategy with the proposed distributed two-tier mechanism to assess execution cost, end-to-end latency, network resource consumption, data rates, packet flow, and packet loss. Quantified results demonstrate that our two-tier approach reduces the average execution cost from 438.61 to 230.48, lowers average latency from 232.34 ms to 129.03 ms, and minimizes average network consumption from 231.26 MB to 129.39 MB. The proposed approach continues to significantly enhance data rates, reduce packet flow processing, decrease packet loss across various routing strategies. Overall, the proposed solution enhances stability, responsiveness, and robustness of V2X communication, making it suitable for future large-scale IoV deployments.

The rapid adoption of cloud computing has introduced critical security challenges in the cloud, with evolving cyberattacks exposing vulnerabilities in conventional intrusion detection systems (IDS). Existing approaches often struggle with high false-positive rates, poor handling of imbalanced traffic, and computational overhead in dynamic cloud environments. To address these issues, we propose SLCAE-BiLSTM, a deep learning-based IDS which enhances feature extraction and sequential learning. The Single-Layer Contractive Autoencoder (SLCAE) ensures efficient data representation by minimizing redundancy while preserving critical attack patterns. Meanwhile, the Bidirectional Long Short-Term Memory (BiLSTM) captures temporal dependencies in network traffic, improving the detection of rare attacks. Experimental evaluations on two benchmark datasets demonstrate SLCAE-BiLSTM's superiority, achieving 99.91% and 99.87% accuracy in binary classification and 97.73% and 91.22% in multi-class classification, surpassing state-of-theart models such as SCAE-SVM, SAE-SVM, and SDAE-SVM. These high accuracy rates indicate a significant reduction in misclassification and improved detection of both common and rare cyber threats. Furthermore, its reduced computational overhead and faster inference time makes it an efficient solution for enhancing cloud security against emerging threats.

The recent advancements in network systems, including Software-Defined Networking (SDN), Network Functions Virtualization (NFV), and cloud networking, have revolutionized network management by increasing efficiency and reducing manual effort. This has led to improved agility in deploying new network services, enabling scaling of network resources, making it easier to handle sudden increases in demand, and efficiently accessing new solutions. However, the heterogeneous network infrastructure and the physical links’ capability still impact the performance of interconnected nodes. This work provides a solution to this problem which centers on the use of Intent-Based Networking (IBN) for a high-level definition of service requirements (QoS) tailored to the specifications of each particular node. Additionally, Graph Neural Network (GNN) is integrated into the proposed system to model the overlay topology and understand the behavior of nodes and links. This allows the defined intents to be translated into optimal paths between end-to-end nodes. The network QoS is constantly monitored, and the GNN model regularly updates the path selection to meet the QoS specified by intents. The solution has been implemented as an IBN system design consisting of a manager for intent definition, a GNN model for optimal path selection, an Off-Platform Application (OPA) for policy creation, and a real-time monitoring system for network state assurance.

With the emergence of Zero Trust (ZT) Architecture, industry leaders have been drawn to the technology because of its potential to handle a high level of security threats. The Zero Trust Architecture (ZTA) is paving the path for a security industrial revolution by eliminating location-based implicant access and focusing on asset, user, and resource security. Software Defined Perimeter (SDP) is a secure overlay network technology that can be used to implement a Zero Trust framework. SDP is a next-generation network technology that allows network architecture to be hidden from the outside world. It also hides the overlay communication from the underlay network by employing encrypted communications. With encrypted information, detecting abnormal behavior of entities on an overlay network becomes exceedingly difficult. Therefore, an automated system is required. We proposed a method in this paper for understanding the normal behavior of deployed polices by mapping network usage behavior to the policy. An Apache Spark collects and processes the streaming overlay monitoring data generated by the built-in fabric API in order to do this mapping. It sends extracted metrics to Prometheus for storage, and then uses the data for machine learning training and prediction. The cluster-id of the link that it belongs to is predicted by the model, and the cluster-ids are mapped onto the policies. To validate the legitimacy of policy, the labeled polices hash is compared to the actual polices hash that is obtained from blockchain. Unverified policies are notified to the SDP controller for additional action, such as defining new policy behavior or marking uncertain policies.

Due to the mega-scale of next-generation networks, dynamic network policy management and assessment is a continuous struggle for network operators. As a result, networks have evolved towards automated Softwarized intent-based policy platforms. IBN (Intent-based Networking) administers high-level interpretation of intents and translates them as policies to the lower-level infrastructures. As a Softwarized platform, it defines an SSOT (Single Source of Truth) where every high-level intention has well-defined policy action at every stage of orchestration from top to bottom. However, due to the dynamic nature of the network, the static quantitative measures may result in excessive reservation or under-allocation of network resources. As network service orchestration is performed for the future, deciding on service parameters per resource availability is crucial. Hence, this work utilizes machine-learning-based resource forecasting to determine quantitative measures for intent activation proactively. This work empowers IBN policy activation through LSTM (Long Short-Term Memory) based compute resource forecasting and RouteNet-driven transport network path link utilization forecasting. The goal of IBN is to ensure service level agreement, as there are two primary service activation rules, GBR and Non-GBR (Guaranteed Bit Rate); hence using forecasting, the proposed model ensures the service agreement accomplishment proactively.

Internet of Vehicles (IoV) is an emerging archetype that is a distributed network of various vehicles armed with sensors, actuators, technologies, and applications to connect and exchange data with each other over the Internet. The primary goal of IoV is to provide a vehicular platform to enable better communication and Quality of Service (QoS) for vehicles, pedestrians, and roadside infrastructure in real-time, through the use of Vehicle-to-Vehicle (V2V), Vehicle-to-Pedestrian (V2P), Vehicle-to-Infrastructure (V2I), Vehicle-to-Network (V2N), and Vehicle-to-Cloud (V2C) channels. However, the increasing number of vehicular services poses serious concerns and challenges to the researchers: real-time traffic forecasting, service placement, security, reliability, and routing. The primary focus of this work is concerned with the challenge of multi-regional forecasting of multi-class traffic. These traffic forecasting models enable pro-activeness in systems by providing real-time and accurate predictions. Also, they can explore traffic densities over spatial and temporal domains for various vehicle types. However, current literature cannot provide forecasts for multi-region and multi-class vehicles at the same time. This study aims at enabling a proactive platform which could make decisions based on the integrated Graph Neural Network (GNN) and Gated Recurrent Unit (GRU) based traffic forecasting model, i.e., Spatio-Temporal GNN (STGNN) based traffic forecasting model. The STGNN-based traffic forecasting model uses the GNN and GRU models to explore spatial and temporal features of varying vehicular multi-class traffic densities. In GNN, spatial data consisting of multi-class traffic densities are utilized for the feature extraction that results in graph embeddings. In the GRU, these graph embeddings are utilized for temporal feature extraction. This approach enables the forecasting of multi-class vehicular traffic densities and the pro-activeness of an IoV platform. In addition, the performance results show that an intelligent platform can be built upon the proposed traffic forecasting model that is capable of inspecting complex and nonlinear traffic accurately.

Zero Trust Networking (ZTN) is a security model where no entity in a network infrastructure is trusted. The first bastion of security for achieving ZTN is to have strong identity verification. Several standard methods for assuring a robust identity exist (E.g., OAuth2.0, OpenID Connect). These standards employ the use of JSON Web Tokens (JWT) during the authentication process. However, the use of JWT for One Time Token (OTT) enrollment has a latent security issue. A JWT can be intercepted by a third party and the information of the payload can be exposed, revealing the details of the enrollment server. Furthermore, an intercepted JWT could be used for enrollment by an impersonator as long as the JWT remains active. Our proposed mechanism aims to secure the ownership of the OTT by including the JWT as encrypted metadata into a Non-Fungible Token (NFT). The mechanism uses the blockchain Public Key of the intended owner for encrypting the JWT, and the blockchain assures the JWT ownership by mapping it to the intended owner's blockchain public address. Our proposed mechanism is applied to an emerging Zero Trust framework (OpenZiti) alongside a permissioned Ethereum blockchain using Hyperledger Besu. The Zero Trust Framework provides the enrollment functionality, while our proposed mechanism based on blockchain and NFT assures the secure distribution of OTTs that is used for the enrollment of identities.

Multiple clouds need to share resources as a service for various reasons, such as overcoming single points of failure or reducing latency. Tunneling serves as the mechanism for multiple clouds to share their resources. Generic Network Virtualization Encapsulation (GENEVE) tunnel has the advantage of a flexible header over other L2 tunnels such as GRE or VXLAN. This enables the GENEVE tunnel to transfer a larger payload in case of smaller header size. The service must be acquired from an optimal Cloud and Instance. An optimal Cloud has the best network performance forecasted. An optimal Instance is forecasted not to be overutilized during resource sharing. For this purpose, monitoring tools must be deployed for metering Instances and network links. Data obtained from monitoring tools can be utilized by Machine Learning for Instances and network performance forecasting. Initially, two clouds are deployed at Trans-Eurasia Internetworking (TEIN), one in Malaysia and the other in South Korea. Monitoring tools are deployed for network and Instance performance monitoring. GENEVE tunnel is then deployed on OVS bridges, after which Instances of a cloud can access Instances of the other Cloud for resource sharing.

5G technology come up with many innovative features compared to legacy networks, such as network slicing that envisioned a wide variety of services from different customers, network operators, and industrial verticals. Network slicing ensures dedicated and isolated resources to each of the services. The autonomous orchestration and management of end-to-end (e2e) network slicing is critical due to the complex network configuration for the underlying infrastructure. On the other side, data analytics seems promising to manage and control the underlying network resources proactively. So, network data analytics function (NWDAF) has been introduced in 5G service-based architecture (SBA), which enables network operators to use various artificial intelligence (AI) and machine learning (ML) techniques. Therefore, this paper presents a closed-loop mechanism that has two parts: 1) an intent-based networking (IBN) mechanism for efficient control, orchestration, and management of e2e network slicing. 2) A data analytics mechanism that uses novel hybrid ensemble learning (EL) algorithms for network resource utilization prediction and anomaly detection and mitigation. The results show that the proposed stacking ensemble learning (STEL) model for resource utilization prediction enhanced accuracy by approximately up to 20% and reduced the error by 45% compared to the state-of-the-art models. In addition, ML models assist the IBN platform in updating and managing the network resources proactively.

Network ecosystems have grown to encompass multiple application domains. SDN and NFV technologies have helped pave the road for the evolution of the core and edge networking systems, allowing for numerous services to be served by the same physical infrastructure. Guaranteeing the operability of the network has become an ever-increasing requirement in order to sustain the underlying services deployed on the network. For this, Intent-Based Networking (IBN) aims to abstract network management by introducing high-level rules/policies that are translated to network configurations per service requirements. By following this principle, we proposed an anomaly detection and mitigation mechanism that exploits the characteristics of IBN for collecting and analyzing flows, using Machine Learning for interpreting traffic patterns, and automatic deployment of high-level policies for corrective actions related to anomalous traffic occurrences. The complete system is deployed on the Korea Advanced Research Network (KOREN), where the abstraction provided by IBN for network anomaly detection and mitigation is a key factor in closing the gap to achieve complete network automation.

Networks slicing in 5G network enables the network operators to accommodate the different quality of service (QoS) to their customers. Moreover, the data analytics in 5G mobile network can be seen as a robust solution to transform the challenging features of 5G into a reality. So, for that, the Third Generation Partnership Project (3GPP) has been introduced a network data analytics function (NWDAF) in 5G service-based architecture (SBA). NWDAF collects the data from different core and management domains and performs analytics on that historical data. It also enables the network operators (NOs) to train their Machine Learning (ML) techniques and use various third-party solutions. On the other side, the automation and management of the end - to-end (e2e) network slicing in a multi -domain environment is a critical task. Therefore, in this manuscript, we have designed a closed-loop Intent-based Networking (IBN) platform, which automatically ensures the commissioning, activation, run-time monitoring, and decommissioning of the network slices. Moreover, we have integrated the newly introduced 3GPP NWDAF with the IBN platform for efficient e2e network slice lifecycle management. By implementing different ML models in the NWDAF function, we can predict the slice load, user mobility, traffic forecasts, and anomaly detection from the network slices.

The amount of data generation is exponentially increasing over the past decade due to the widespread use of multimedia applications and social media platforms. Advanced real-time applications such as virtual reality, augmented reality, automated vehicles, smart homes, and intelligent traffic control systems have increased the demand for low latency. Many of these applications are delay-sensitive and put enormous stress on the core network to respond in real-time. CDN (Content Delivery Network) brings storage service to end-users proximity to provide low latency, high data throughput, and low traffic pressure to handle the problems mentioned above. Due to the limited storage capacity of the edge, only in-demand content should cache. Therefore, to optimally utilized the cache space, an efficient content caching and replacement policy is needed. To this end, in this paper, we propose an optimal content replacement algorithm. In this algorithm, a video request pattern is first generated based on a publicly available dataset. After that, a machine learning model is trained on cache logs data. As a result, the predicted video is deleted from the edge to make space for new videos. A real-time testbed is built on KOREN to check the performance of our model. The results based on MAE, MSE, and R-2 show that our model performs well in real-time scenarios.

The expansion of infrastructure and services in the 5th generation networks resulted in complex configuration management throughout the network lifecycle. To this end, network automation replaces existing traditional manual administrative approaches with software-driven repetitive and reliable applications. Since network expansion is in multiple dimensions, including multi-services, domains, and platforms, it is challenging to resolve such a vast infrastructure through a single automation solution. Hence this paper proposed applying an intent-based solution for achieving automatic orchestration for vastly spreading network services. Intent-based solution not only considers network automation but also performs service assurance throughout the network service lifecycle. The proposed IBN (Intent-Based Networking) solution implements a closed-loop network lifecycle management using a single abstracted software platform. It translates high-level requirements to the infrastructure irrespective of the various underlying platforms and domains, and it includes intelligence-driven monitoring and updates for service assurance. A multi-model machine learning approach is proposed in this work to control the network infrastructure reliably. To this end LSTM (Long Short-Term Memory) algorithm is applied for compute-resource prediction and the Route-Net model for optimized service path routing. The infrastructure includes FlexRAN deployed as the access network controller, OSM (OpenSource MANO) resides at the core, and the KOREN network serves as a high-speed transport network.

Nowadays, intelligence is paving its ways into the IoV domain. With the need of improvement in intelligence in this area, the need of self-organizing network management systems for providing V2X communication is also of vital importance, which current systems lack. Current routing solutions for IoV are complex and require intelligence embedded in the form of closed-loop systems. To this, an intent-based system is designed, which takes high level requirements for routing in IoV.The routing approach is road-aware and widens the scope of road-awareness to vehicles from multiple edge domains. Another problem with the current routing schemes in IoV is that their network management systems are not self-organizing. A self-organizing management system is of high importance and requires a closed-loop system. To this, an intent-based approach is followed integrated with a reinforcement learning model that supports the creation of a routing policy, which is further applied to the orchestrator and enables a road-aware and hierarchical routing approach. The proposed system is shown to be efficient in terms of data rate and the number of packet flows managed per unit time, in the management of vehicular networks.

Intent-Based Networking (IBN) is a model that enables proactive network control and automation to satisfy high-level demands. It follows a closed-loop mechanism that abstracts the network complexity and dynamically allows updates to the network monitoring and intelligence. Hence, it eliminates the traditionally practiced error-prone manual network control. In contrast with the traditional reactive approach, IBN-based approach promotes the proactive and dynamic solution. To this end, IBN uses machine learning (ML) to predict the future and keeps a balance between the intended and actual state. Hence, this work considers an IBN-driven routing mechanism to ensure service provisioning with the desired quality of service (QoS). In contrast to the traditional static routing scheme, this work considers AI-driven dynamic and proactively updatable routing schemes to ensure QoS. This work demonstrates AI-driven service route control mechanism for ensuring quality of service (QoS) on top of Korea Advanced Research Network-Software Defined Infrastructure (KOREN-SDI). More precisely, an ML-based approach is used to find the best path between source and destination nodes. In addition, it incorporates IBN closed-loop process to monitor and update service routes proactively based on predicted future link utilization. This novel approach introduces proactive updates on runtime to avoid future failures and ensures seamless service provisioning with the fulfillment of QoS demands by changing traffic routes dynamically.

Network slicing is an important pillar of 5G networks that empowers the network operators to provide the different quality of services (QoS) to the users. It enables network operators to split the physical network into multiple logical networks to meet different QoS requirements. In this research paper, we have designed an intent-based network slicing framework that can slice and manage the core network and radio access network (RAN) resources efficiently. It is an automated system, where users just needs to provide higher-level information in the form of intents/contracts for a network slice, and in return our system deploys and configures the requested resources. Moreover, a deep learning model Generative Adversarial Neural Network (GAN) has been used for the management of network resources. Several tests have been performed by creating three slices with our system, which shows better performance in terms of bandwidth and latency.

5G claims the service provisioning for a variety of new applications increasing orchestration complexity. Additionally, many different orchestrators and platforms have been developed over recent years to handle and control the network infrastructures. However, each of the orchestration platforms requires different expertise for handling them. The diversity and dynamicity in terms of requirements made it very complex for the operators to handle runtime operations. As with the increasing number of devices, the amount of traffic streamed over network deviates drastically making it impossible for network operators to handle the network operations manually. Hence automated platforms are a dire need, this work proposed a generic intent-based system that can automatically orchestrate and manage network lifecycle over multiple domains, sites and orchestrators. This system will include the orchestration over multiple domains access, transport and core and it also includes the use of Machine Learning for prediction of resource status for proactive decision making to manage the slice lifecycle automatically.

To achieve high availability of traffic and maximum coverage in the 5G network, clusters of small cells approach to maximize the system capacity. In this approach, the Core Network (CN) encounters a large number of signals due to the frequent hand-off between small cells in the event of an increase in the speed of user movement and users within certain limits of small cells. The present 5G system executes all handoff signals in the traditional complex hierarchical sequence which leads the system toward the bad performance due to redundant signaling. NO Stack architecture has emerged as a promising methodology for radio access network (RAN) in the context of reducing the redundant signaling during X2-based handover by using control logic centrally and flat protocols. This paper proposes a system that is based on NO Stack architecture for reducing handoff requests in CN by assigning the dedicated mobility controller to each cluster of small cells for locally managing the mobility. The dedicated cluster controller specially designs to manage and control the cluster of small cells as well as maintain the forwarding information of user equipment locally. In this proposed system, the dedicated controller resides at the nearest edge cloud, therefore a distributed edge cloud network is developed for high computing infrastructure and low latency access for the X2-based handover request.

Flying networks are resource constraints while the nature of nodes' mobility is very dynamic and unpredicted. Therefore, these networks are very prone to link failure and performance degradation. By considering the existing limitations, this work proposes a new approach consists of proactive and reactive network failure mitigation techniques that have been named as a hybrid approach. In the proposed architecture, the SDN controllers are distributed where each one controls its local domain nodes. The controller node continuously monitors the network state information and proactively adjusts the near-future changes to the topology. Each local domain also contains a sink node that directly connects to the controller. The sink node is used to forward the network state information to the controller and keep the controller defined flow rules for local domain nodes. The sink node can also request a new path in case of any link failure or any topology updates cause by nodes' movement. Besides, a distributed routing protocol also runs on domain nodes to establish connectivity toward the sink node.

5G networks are aimed at provisioning of a wide range of sophisticated services with uninterrupted user experience. In addition, it is very challenging to manage all the services due the variety in the service requirement and a very large number of users causing dynamic changes in traffic streamed over the network. Currently, the networks are managed manually and requires experts to control the behavior of network. Due to increase in network domain it is an esteem requirement to manage and control network autonomously. In this paper we have introduced and Intent-Based networking approach which on one side abstracts and automates the network configuration also it assures the network resource state stability by using machine learning. We have used an IBN abstraction layer and M-CORD as an next generation testbed for the implementation of this work.

The enormous increase in the number of communicating vehicles every year imposes many challenges for efficient resource allocation in IoV (Internet of Vehicles). Additionally, with advancements, many new communication services have been added to the vehicles, each requiring a unique set of resources. The vehicle communication services include simple video streaming, multimedia, and navigation to mission-critical services in self-driving cars. In addition to that, the vehicular technology is rapidly shifting towards the electric vehicle to help the green energy revolution and reduction of carbon footprints. Similarly, communication services are also consuming high-energy so IoE (Internet of Energy) has also become vital to optimize resource utilization for reduction of energy consumption. Due to the complexity of the service requirements in IoV, an energy-efficient and intelligent resource allocation system is essential. To this end, this paper proposes a solution that provides an efficient and proactive resource orchestration for IoV services while considering edge-cloud infrastructure. Machine Learning (ML) approach has been used to manage the resources by predicting network traffic at the edge, and VNF resource utilization at the core.

Monitoring of infrastructural resources against a huge number of virtual machines in clouds is a challenging task to system administrators. System management tools are used to collect and provide the utilization of a variety of performance counters of each virtual machine. Such dispersed views cannot reveal the actual performance behavior of virtual machines. To this purpose, a monitoring framework is necessary particularly since cloud hosts are subject to varying load conditions. In this paper, we propose a framework based on sFlow to interpret the resource utilization correctly and in real-time to ease the workload of system administrators.

Elephant flows in data center networks have a tendency to utilize a lot of bandwidth, leaving latency-sensitive mice flows choked behind them. This causes degradation of application performance running on the network. Therefore, a data center network should be able to not only identify elephant flows, but also offer Quality of Service (QoS) provisioning. SDN makes network operating systems to achieve greater governance of the control plane within a given network. In order to detect elephant flows, we use a framework based on sFlow sampling technology. We show an approach to guarantee QoS that is administered and defined by a centralized network controller by using SDN and specifications offered by OpenFlow. Although there have been efforts in this area over the past but in the scope of this paper, we will only focus on how Traffic Shaping (rate limiting) based classification technique can be used within an SDN network.