Single Failure Routing Protection Algorithm in the Hybrid SDN Network

Loop free alternate (LFA) is a routing protection scheme that is currently deployed in commercial routers. However, LFA cannot handle all single network component failure scenarios in traditional networks. As Internet service providers have begun to deploy software defined network (SDN) technology, the Internet will be in a hybrid SDN network where traditional and SDN devices coexist for a long time. Therefore, this study aims to deploy the LFA scheme in hybrid SDN network architecture to handle all possible single network component failure scenarios. First, the deployment of LFA scheme in a hybrid SDN network is described as a 0-1 integer linear programming (ILP) problem. Then, two greedy algorithms, namely, greedy algorithm for LFA based on hybrid SDN (GALFAHSDN) and improved greedy algorithm for LFA based on hybrid SDN (IGALFAHSDN), are proposed to solve the proposed problem. Finally, both algorithms are tested in the simulation environment and the real platform. Experiment results show that GALFAHSDN and IGALFAHSDN can cope with all single network component failure scenarios when only a small number of nodes are upgraded to SDN nodes. The path stretch of the two algorithms is less than 1.36.     

HDLIDP: A Hybrid Deep Learning Intrusion Detection and Prevention Framework

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.     

Quality of Service Improvement with Optimal Software-Defined Networking Controller and Control Plane Clustering

The controller is indispensable in software-defined networking (SDN). With several features, controllers monitor the network and respond promptly to dynamic changes. Their performance affects the quality-of-service (QoS) in SDN. Every controller supports a set of features. However, the support of the features may be more prominent in one controller. Moreover, a single controller leads to performance, single-point-of-failure (SPOF), and scalability problems. To overcome this, a controller with an optimum feature set must be available for SDN. Furthermore, a cluster of optimum feature set controllers will overcome an SPOF and improve the QoS in SDN. Herein, leveraging an analytical network process (ANP), we rank SDN controllers regarding their supporting features and create a hierarchical control plane based cluster (HCPC) of the highly ranked controller computed using the ANP, evaluating their performance for the OS3E topology. The results demonstrated in Mininet reveal that a HCPC environment with an optimum controller achieves an improved QoS. Moreover, the experimental results validated in Mininet show that our proposed approach surpasses the existing distributed controller clustering (DCC) schemes in terms of several performance metrics i.e., delay, jitter, throughput, load balancing, scalability and CPU (central processing unit) utilization.     

A Safe and Reliable Routing Mechanism of LEO Satellite Based on SDN

Satellite networks have high requirements for security and data processing speed. In order to improve the reliability of the network, software-defined network (SDN) technology is introduced and a central controller is set in the network. Due to the characteristics of global perspective, control data separation, and centralized control of SDN, the idea of SDN is introduced to the design of the satellite network model. As a result, satellite nodes are only responsible for data transmission, while the maintenance of the links and the calculation of routes are implemented by the controller. For the massive LEO satellite network based on SDN, a state evaluation decision routing mechanism is proposed. The designed mechanism monitors the status of the entire network effectively and reduces the on-board load on the satellite network. The best routing decision is made under the comprehensive consideration of the current and historical status of each intersatellite link between Low Earth Orbit (LEO) satellite network nodes. The calculation and storage requirements are controlled within a reasonable range. Based on the curve parameter transmission fuzzy encryption algorithm, a safe and reliable condition assessment decision routing mechanism (CADRM) is designed. It ensures that the personal information of the LEO satellite network can be transmitted safely and effectively. The experimental simulation results show the improvement of network throughput, the reduction of packet loss rate and the enhancing of network reliability.     

On the performance of time division multiple access-based multihop fixed cellular networks with respect to available frequency carriers

In multihop cellular networks (MCN), the user nodes can act as relays and forward other nodes' traffic to/from base stations. There are several advantages of MCN such as the improved signal quality and higher coverage. However, it is known that multihop relaying networks require extra radio resources. Therefore the performance of MCN depends to a great extent on the availability of adequate radio resources. The performance of a time division multiple access (TDMA)-based multihop fixed cellular network is analysed with highlighting the dependence of the system performance on the amount of available radio resources, namely, the number of frequency carriers. Results show that in a fixed cellular network, the multihop architecture significantly outperforms the traditional single-hop architecture in terms of the outage probability and throughput if an adequate amount of frequency carriers is available in the network. Otherwise, the multihop fixed cellular networks architecture loses its superiority and might even lead to performance degradation, particularly at high loading levels.     

Artificial Intelligence Based Reliable Load Balancing Framework in Software-Defined Networks

Software-defined networking (SDN) plays a critical role in transforming networking from traditional to intelligent networking. The increasing demand for services from cloud users has increased the load on the network. An efficient system must handle various loads and increasing needs representing the relationships and dependence of businesses on automated measurement systems and guarantee the quality of service (QoS). The multiple paths from source to destination give a scope to select an optimal path by maintaining an equilibrium of load using some best algorithms. Moreover, the requests need to be transferred to reliable network elements. To address SDN’s current and future challenges, there is a need to know how artificial intelligence (AI) optimization techniques can efficiently balance the load. This study aims to explore two artificial intelligence optimization techniques, namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), used for load balancing in SDN. Further, we identified that a modification to the existing optimization technique could improve the performance by using a reliable link and node to form the path to reach the target node and improve load balancing. Finally, we propose a conceptual framework for SDN futurology by evaluating node and link reliability, which can balance the load efficiently and improve QoS in SDN.     

Performance Evaluation of Virtualization Methodologies to Facilitate NFV Deployment

The development of the Next-Generation Wireless Network (NGWN) is becoming a reality. To conduct specialized processes more, rapid network deployment has become essential. Methodologies like Network Function Virtualization (NFV), Software-Defined Networks (SDN), and cloud computing will be crucial in addressing various challenges that 5G networks will face, particularly adaptability, scalability, and reliability. The motivation behind this work is to confirm the function of virtualization and the capabilities offered by various virtualization platforms, including hypervisors, clouds, and containers, which will serve as a guide to dealing with the stimulating environment of 5G. This is particularly crucial when implementing network operations at the edge of 5G networks, where limited resources and prompt user responses are mandatory. Experimental results prove that containers outperform hypervisor-based virtualized infrastructure and cloud platforms’ latency and network throughput at the expense of higher virtualized processor use. In contrast to public clouds, where a set of rules is created to allow only the appropriate traffic, security is still a problem with containers.     

Kernel Search-Framework for Dynamic Controller Placement in Software-Defined Network

In software-defined networking (SDN) networks, unlike traditional networks, the control plane is located separately in a device or program. One of the most critical problems in these networks is a controller placement problem, which has a significant impact on the network’s overall performance. This paper attempts to provide a solution to this problem aiming to reduce the operational cost of the network and improve their survivability and load balancing. The researchers have proposed a suitable framework called kernel search introducing integer programming formulations to address the controller placement problem. It demonstrates through careful computational studies that the formulations can design networks with much less installation cost while accepting a general connected topology among controllers and user-defined survivability parameters. The researchers used the proposed framework on six different topologies then analyzed and compared with Iterated Local Search (ILS) and Expansion model for the controller placement problem (EMCPP) along with considering several evaluation criteria. The results show that the proposed framework outperforms the ILS and EMCPP. Thus, the proposed framework has a 38.53% and 38.02% improvement in reducing network implementation costs than EMCPP and ILS, respectively.     

DDoS Detection in SDN using Machine Learning Techniques

Software-defined network (SDN) becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network infrastructure. The SDN controller is considered as the operating system of the SDN based network infrastructure, and it is responsible for executing the different network applications and maintaining the network services and functionalities. Despite all its tremendous capabilities, the SDN face many security issues due to the complexity of the SDN architecture. Distributed denial of services (DDoS) is a common attack on SDN due to its centralized architecture, especially at the control layer of the SDN that has a network-wide impact. Machine learning is now widely used for fast detection of these attacks. In this paper, some important feature selection methods for machine learning on DDoS detection are evaluated. The selection of optimal features reflects the classification accuracy of the machine learning techniques and the performance of the SDN controller. A comparative analysis of feature selection and machine learning classifiers is also derived to detect SDN attacks. The experimental results show that the Random forest (RF) classifier trains the more accurate model with 99.97% accuracy using features subset by the Recursive feature elimination (RFE) method.     

Sea Turtle Foraging Optimization-Based Controller Placement with Blockchain-Assisted Intrusion Detection in Software-Defined Networks

Software-defined networking (SDN) algorithms are gaining increasing interest and are making networks flexible and agile. The basic idea of SDN is to move the control planes to more than one server’s named controllers and limit the data planes to numerous sending network components, enabling flexible and dynamic network management. A distinctive characteristic of SDN is that it can logically centralize the control plane by utilizing many physical controllers. The deployment of the controller—that is, the controller placement problem (CPP)—becomes a vital model challenge. Through the advancements of blockchain technology, data integrity between nodes can be enhanced with no requirement for a trusted third party. Using the latest developments in blockchain technology, this article designs a novel sea turtle foraging optimization algorithm for the controller placement problem (STFOA-CPP) with blockchain-based intrusion detection in an SDN environment. The major intention of the STFOA-CPP technique is the maximization of lifetime, network connectivity, and load balancing with the minimization of latency. In addition, the STFOA-CPP technique is based on the sea turtles’ food-searching characteristics of tracking the odour path of dimethyl sulphide (DMS) released from food sources. Moreover, the presented STFOA-CPP technique can adapt with the controller’s count mandated and the shift to controller mapping to variable network traffic. Finally, the blockchain can inspect the data integrity, determine significantly malicious input, and improve the robust nature of developing a trust relationship between several nodes in the SDN. To demonstrate the improved performance of the STFOA-CPP algorithm, a wide-ranging experimental analysis was carried out. The extensive comparison study highlighted the improved outcomes of the STFOA-CPP technique over other recent approaches.     

Resource Allocation for Throughput Maximization in Cognitive Radio Network with NOMA

Spectrum resources are the precious and limited natural resources. In order to improve the utilization of spectrum resources and maximize the network throughput, this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonal multiple access (CRN-NOMA). NOMA, as the key technology of the fifth-generation communication (5G), can effectively increase the capacity of 5G networks. The optimization problem proposed in this paper aims to maximize the number of secondary users (SUs) accessing the system and the total throughput in the CRN-NOMA. Under the constraints of total power, minimum rate, interference and SINR, CRN-NOMA throughput is maximized by allocating optimal transmission power. First, for the situation of multiple sub-users, an adaptive optimization method is proposed to reduce the complexity of the optimization solution. Secondly, for the optimization problem of nonlinear programming, a maximization throughput optimization algorithm based on Chebyshev and convex (MTCC) for CRN-NOMA is proposed, which converts multi-objective optimization problem into single-objective optimization problem to solve. At the same time, the convergence and time complexity of the algorithm are verified. Theoretical analysis and simulation results show that the algorithm can effectively improve the system throughput. In terms of interference and throughput, the performance of the sub-optimal solution is better than that of orthogonal-frequency-division-multiple-access (OFDMA). This paper provides important insights for the research and application of NOMA in future communications.     

An IoT Environment Based Framework for Intelligent Intrusion Detection

Software-defined networking (SDN) represents a paradigm shift in network traffic management. It distinguishes between the data and control planes. APIs are then used to communicate between these planes. The controller is central to the management of an SDN network and is subject to security concerns. This research shows how a deep learning algorithm can detect intrusions in SDN-based IoT networks. Overfitting, low accuracy, and efficient feature selection is all discussed. We propose a hybrid machine learning-based approach based on Random Forest and Long Short-Term Memory (LSTM). In this study, a new dataset based specifically on Software Defined Networks is used in SDN. To obtain the best and most relevant features, a feature selection technique is used. Several experiments have revealed that the proposed solution is a superior method for detecting flow-based anomalies. The performance of our proposed model is also measured in terms of accuracy, recall, and precision. F1 rating and detection time Furthermore, a lightweight model for training is proposed, which selects fewer features while maintaining the model’s performance. Experiments show that the adopted methodology outperforms existing models.     

LOA-RPL: Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

Energy conservation is a significant task in the Internet of Things (IoT) because IoT involves highly resource-constrained devices. Clustering is an effective technique for saving energy by reducing duplicate data. In a clustering protocol, the selection of a cluster head (CH) plays a key role in prolonging the lifetime of a network. However, most cluster-based protocols, including routing protocols for low-power and lossy networks (RPLs), have used fuzzy logic and probabilistic approaches to select the CH node. Consequently, early battery depletion is produced near the sink. To overcome this issue, a lion optimization algorithm (LOA) for selecting CH in RPL is proposed in this study. LOA-RPL comprises three processes: cluster formation, CH selection, and route establishment. A cluster is formed using the Euclidean distance. CH selection is performed using LOA. Route establishment is implemented using residual energy information. An extensive simulation is conducted in the network simulator ns-3 on various parameters, such as network lifetime, power consumption, packet delivery ratio (PDR), and throughput. The performance of LOA-RPL is also compared with those of RPL, fuzzy rule-based energy-efficient clustering and immune-inspired routing (FEEC-IIR), and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm (RISA-RPL). The performance evaluation metrics used in this study are network lifetime, power consumption, PDR, and throughput. The proposed LOA-RPL increases network lifetime by 20% and PDR by 5%–10% compared with RPL, FEEC-IIR, and RISA-RPL. LOA-RPL is also highly energy-efficient compared with other similar routing protocols.     

A Decision-Based Hybrid Proxy Mobile IPv6 Scheme for Better ResourcesUtilization

Seamless mobility is always one of the major requirements of modern-day communication. In a heterogeneous and massive IoT environment, efficient network-based mobility protocol such as proxy mobile IPv6 (PMIPv6), is potentially a good candidate for efficient mobility as well as resource utilization efficiency. Several extensions are devised for performance in the research domain. However, a multi-criterion decision-based resource-efficient PMIPv6 extension is required to achieve efficiency when network resources are overloaded. In this research, a multi-criterion decision-based PMIPv6 scheme is devised that provides better performance when the Local Mobility Anchor (LMA) or Mobile Access Gateway (MAG) is overloaded. The objective is achieved by monitoring the load status of MAG or LMA and based on their status, the proposed scheme adapts itself to provide seamless mobility in addition to optimal efficiency. The proposed scheme is compared with the existing LMA and MAG-based mobility management protocol extensions. Based on the analysis of the comparison, the obtained results prove that providing a decision-based PMIPv6 scheme is better for service continuity as well as optimal performance in the context of required buffering, handover efficiency, and necessary signaling cost.     

Energy Efficient Ambience Awake Routing with OpenFlow Approach

A major problem in networking has always been energy consumption. Battery life is one parameter which could help improve Energy Efficiency. Existing research on wireless networking stresses on reducing signaling messages or time required for data transfer for addressing energy consumption issues. Routing or Forwarding packets in a network between the network elements like routers, switches, wireless access points, etc., is complex in conventional networks. With the advent of Software Defined Networking (SDN) for 5G network architectures, the distributed networking has embarked onto centralized networking, wherein the SDN Controller is responsible for decision making. The controller pushes its decision onto the network elements with the help of a control plane protocol termed OpenFlow. Decentralized networks have been largely in use because of their ease in physical and logically setting the administrative hierarchies. The centralized controller deals with the policy funding and the protocols used for routing procedures are designated by the decentralized controller. Ambience Awake is a location centered routing protocol deployed in the 5G network architecture with OpenFlow model. The Ambience Awake mechanism relies on the power consumption of the network elements during the packet transmission for unicast and multicast scenarios. The signalling load and the routing overhead witnessed an improvement of 30% during the routing procedure. The proposed routing mechanism run on the top of the decentralized SDN controller proves to be 19.59% more efficient than the existing routing solutions.     

一种用于Ad Hoc网络的简单跨层流控机制及其性能分析

为了改善Ad Hoc网络中传输层协议的性能,基于跨层设计思想设计了一种简单的流控机制.该机制有效地利用了网络层路由反馈、传输层协议、应用层速率自适应调节之间的关联性和跨层信息交互,应用层可根据网络层反馈的路由信息来调节发送速率,进而将网络负载维持在合理水平以最大化网络吞吐量.通过计算机模拟评价了此跨层流控机制,模拟评价结果表明,该机制能够使上层应用得到更加智能的控制,改善了基于UDP和TCP的业务性能,尤其是在网络负载较低的情况下效果更为明显.     

Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

In software-defined networks (SDNs), controller placement is a critical factor in the design and planning for the future Internet of Things (IoT), telecommunication, and satellite communication systems. Existing research has concentrated largely on factors such as reliability, latency, controller capacity, propagation delay, and energy consumption. However, SDNs are vulnerable to distributed denial of service (DDoS) attacks that interfere with legitimate use of the network. The ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design, especially in critical applications such as military, health care, and financial services networks requiring high availability. We propose a mathematical model for planning the deployment of SDN smart backup controllers (SBCs) to preserve service in the presence of DDoS attacks. Given a number of input parameters, our model has two distinct capabilities. First, it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating conditions. Second, it recommends an optimal number of smart backup controllers for use with different levels of DDoS attacks. The goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the parameters. Our simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.     

Probe Attack Detection Using an Improved Intrusion Detection System

The novel Software Defined Networking (SDN) architecture potentially resolves specific challenges arising from rapid internet growth of and the static nature of conventional networks to manage organizational business requirements with distinctive features. Nevertheless, such benefits lead to a more adverse environment entailing network breakdown, systems paralysis, and online banking fraudulence and robbery. As one of the most common and dangerous threats in SDN, probe attack occurs when the attacker scans SDN devices to collect the necessary knowledge on system susceptibilities, which is then manipulated to undermine the entire system. Precision, high performance, and real-time systems prove pivotal in successful goal attainment through feature selection to minimize computation time, optimize prediction performance, and provide a holistic understanding of machine learning data. As the extension of astute machine learning algorithms into an Intrusion Detection System (IDS) through SDN has garnered much scholarly attention within the past decade, this study recommended an effective IDS under the Grey-wolf optimizer (GWO) and Light Gradient Boosting Machine (LightGBM) classifier for probe attack identification. The InSDN dataset was employed to train and test the proposed IDS, which is deemed to be a novel benchmarking dataset in SDN. The proposed IDS assessment demonstrated an optimized performance against that of peer IDSs in probe attack detection within SDN. The results revealed that the proposed IDS outperforms the state-of-the-art IDSs, as it achieved 99.8% accuracy, 99.7% recall, 99.99% precision, and 99.8% F-measure.     

Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

Artificial intelligence (AI) techniques have received significant attention among research communities in the field of networking, image processing, natural language processing, robotics, etc. At the same time, a major problem in wireless sensor networks (WSN) is node localization, which aims to identify the exact position of the sensor nodes (SN) using the known position of several anchor nodes. WSN comprises a massive number of SNs and records the position of the nodes, which becomes a tedious process. Besides, the SNs might be subjected to node mobility and the position alters with time. So, a precise node localization (NL) manner is required for determining the location of the SNs. In this view, this paper presents a new quantum bird migration optimizer-based NL (QBMA-NL) technique for WSN. The goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor nodes. The QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating season. In addition, an objective function is derived based on the received signal strength indicator (RSSI) and Euclidean distance from the known to unknown SNs. For demonstrating the improved performance of the QBMA-NL technique, a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques.     

Cooperative wireless networks for better green services

During the last two decades, mobile communication systems (such as GSM, GPRS and 3G networks), wireless broadcasting networks, wireless local area networks (WLAN or WiFi), and wireless sensor networks have been successfully developed and widely deployed through different technological routes for providing a variety of communication services in different application scenarios. While making tremendous contributions to social progress and economic growth, these heterogeneous wireless networks consume a lot of energy in achieving overlapped service coverage, and at the same time, generate strong electromagnetic interference (EMI) and radiation pollution, especially in big cities with high building density and user population. In order to guarantee the overall return on investment (ROI), improve user experience and quality of service (QoS), save energy, reduce EMI and radiation pollution, and enable the sustainable deployment of new profitable applications and services, this paper proposes a cross-network cooperation mechanism to effectively share network resources and infrastructures, and then adaptively control and match multi-network energy distribution characteristics according to actual user/service requirements in different geographic areas. Some idle or lightly-loaded Base Stations (BS or BSs) will be temporally turned off for saving energy and reducing EMI. Initial simulation results show the proposed approach can significantly improve the overall energy efficiency and QoS performance across multiple cooperative wireless networks.