Software Defined Networking Architecture Research Articles

Collaborative prediction and detection of DDoS attacks in edge computing: A deep learning-based approach with distributed SDN

Edge computing (EC) has greatly facilitated the deployment of networked services with fast responses and low bandwidth, by deploying computing and storage at the network edge which is closer to the data sources. However, it is challenging to have the EC servers gain security protections like those in centralized data centers, making them more vulnerable to security attacks, especially distributed denial-of-service (DDoS) attacks. Existing detection approaches relying on the feedback from EC servers under the attacks can incur high bandwidth costs and service performance degradation. In this paper, we propose a new framework CoWatch for collaborative prediction and detection of DDoS Attacks in EC scenarios. Based on the distributed software-defined networking (SDN) architecture, CoWatch can collaboratively predict the DDoS attacks towards the EC servers and detect the attack flows in time. To efficiently filter the suspicious flows in distributed SDN, we devise an optimal threshold model by balancing the trade-off between collaboration efficiency and prediction effectiveness. We also explore and build on the LSTM model to design an algorithm for collaborative prediction and detection of DDoS Attacks. Experiment results on a number of datasets demonstrate the promising performance of CoWatch in effectiveness and efficiency.

Low Rate DDoS Detection Using Weighted Federated Learning in SDN Control Plane in IoT Network

The Internet of things (IoT) has opened new dimensions of novel services and computing power for modern living standards by introducing innovative and smart solutions. Due to the extensive usage of these services, IoT has spanned numerous devices and communication entities, which makes the management of the network a complex challenge. Hence it is urgently needed to redefine the management of the IoT network. Software-defined networking (SDN) intrinsic programmability and centralization features simplify network management, facilitate network abstraction, ease network evolution, has the potential to manage the IoT network. SDN’s centralized control plane promotes efficient network resource management by separating the control and data plane and providing a global picture of the underlying network topology. Apart from the inherent benefits, the centralized SDN architecture also brings serious security threats such as spoofing, sniffing, brute force, API exploitation, and denial of service, and requires significant attention to guarantee a secured network. Among these security threats, Distributed Denial of Service (DDoS) and its variant Low-Rate DDoS (LR-DDoS), is one of the most challenging as the fraudulent user generates malicious traffic at a low rate which is extremely difficult to detect and defend. Machine Learning (ML), especially Federated Learning (FL), has shown remarkable success in detecting and defending against such attacks. In this paper, we adopted Weighted Federated Learning (WFL) to detect Low-Rate DDoS (LR-DDoS) attacks. The extensive MATLAB experimentation and evaluation revealed that the proposed work ignites the LR-DDoS detection accuracy compared with the individual Neural Networks (ANN) training algorithms, existing packet analysis-based, and machine learning approaches.

Blockchain-enhanced cross-ISP spectrum assignment framework in SDONs: SpectrumChain

Huge potentials of optical networks provide an excellent choice in meeting the exponential growth in bandwidth demand. In particular, Elastic Optical Networks (EONs), with its promising potentials in increased availability, failure resilience, load-balancing, and efficient resource allocations, are good candidates for the future high-speed networks. When EONs are augmented with the emerging Software Defined Networking (SDN) architecture, which decouples the data and control planes, an agile and synergistic combination emerges under a general term of Software Defined Optical Networking (SDON), albeit with operational challenges such optimal and efficient spectrum assignments. In this paper, we present a blockchain-enhanced QoS-concentrated cross-Internet Service Provider (ISP) spectrum assignment framework for SDONs, SpectrumChain (SC), to eliminate centralized mediators while coordinating QoS-based inter-ISP traffic. This study introduces a new blockchain use-case with reduced QoS signaling overhead while performing inter-ISP routing in SDONs. We also experience the proposed framework under Hop-by-Hop Wavelength Switching (HWS), Border-Node-Only Wavelength Switching (BWS), and No Wavelength Switching (NWS) scenarios to analyze its performance. Our extensive simulation results show that the SC framework can effectively handle the QoS-enabled inter-ISP routing in SDON architecture in terms of Flow Setup Time (FST), Messages Processed (MEP), Requests Serviced (RS), and Acceptance Ratio (AR) metrics under HWS, BWS, and NWS scenarios.

A Neutrosophic Model for Ranking Technical Sol utions for Three Types of ARP Attacks in SDN Architecture

Software-defined networks (SDN) have developed an understanding of the technological world in recent decades, which has led scholars to become interested in its problems. One of the primary issues facing SDN networks is security. We discovered that ARP assaults constitute a significant threat to SDN, so we provided in this survey the most recent solutions put forth to counteract these attacks, and rank the technical solutions based on the neutrosophic set. The neutrosophic set is used to deal with uncertain data in the evaluation process. The neutrosophic set is integrated with the TOPSIS method to obtain the rank of the proposed solutions. The TOPSIS method is used to give a rank of alternatives with specified criteria. This will make it easier for future researchers to identify and combat the three different forms of ARP attacks—ARP flooding assault, ARP spoofing attack, and ARP broadcasting attack. Prior to that, we went into more detail on SDN networks, including their design and, in particular, the shortcomings of the ARP protocol. Since SDN focuses on separating the controller plane from the data plane and centralizing the controller, it differs significantly from traditional networks and has stirred considerable controversy in the networking industry. Due to the fact that SDN is software-based, it offers greater flexibility, scalability, programmatic management, and control. The decoupling of the control and forwarding planes also enables SDN to connect to applications via application programming interfaces (APIs), supporting application security and performance and resulting in a scalable and dynamic network architecture. Contrarily, because traditional networks are hardware-based, they must use fixed functions and specialized hardware and devices to control the network. As a result, scaling traditional networks requires purchasing new hardware, which is a common issue. The SDN network architecture and its properties, as well as the most significant issues—particularly the three different types of ARP attacks that affect SDN—are covered in some sections of this research. These sections also discuss the best current remedies for these issues and outline the ongoing work that will eventually result in an ideal SDN network architecture free of significant security issues.

Secure Time-Sensitive Software-Defined Networking in Vehicles

Current designs of future In-Vehicle Networks (IVN) prepare for switched Ethernet backbones, which can host advanced LAN technologies such as IEEE Time-Sensitive Networking (TSN) and Software-Defined Networking (SDN). In this paper, we present an integrated Time-Sensitive Software-Defined Networking (TSSDN) architecture that simultaneously enables control of synchronous and asynchronous real-time and best-effort communication for all IVN traffic classes. Despite the central SDN controller, we can validate that control can operate without a delay penalty for TSN traffic, provided protocols are properly mapped. We demonstrate how TSSDN adaptably and reliably enhances network security for in-vehicle communication. A systematic investigation of the possible control flow integrations with switched Ether-networks reveals that these strategies allow for shaping the attack surface of a software-defined IVN. We discuss embeddings of control flow identifiers on different layers, covering the range from a fully exposed mapping to deep encapsulation. We experimentally evaluate these strategies in a production vehicle, which we map to a modern Ethernet topology. Our findings indicate that visibility of automotive control flows on lower network layers enables isolation and access control throughout the network infrastructure. Such a TSSDN backbone can establish and survey trust zones within the IVN and reduce the attack surface of connected cars in various attack scenarios.

Understanding Issues and Challenges of DFR Implementation in SDN Platform

Software-Defined Networking (SDN) is an evolutionary networking paradigm that offers simplified and agile network configuration and management capabilities. However, embracing this new and futuristic paradigm requires the understanding of Digital Forensics (DF) limitations that it presents. Studies show that the dynamism of SDN architecture impedes the preservation of Potential Digital Evidence (PDE) during a Digital Forensic Readiness (DFR) process. Therefore, the identification and acquisition of viable PDE in SDN platforms largely depends on the thorough understanding of the issues and challenges affecting the application of DFR in SDN platforms. For this reason, this study leverages a case study research methodology to empirically underline the forensic limitations and provide level of specificity with which these limitations affect the DFR process. The results of the case study combined with existing literature are used to expose the issues and challenges in a typical SDN testbed. The knowledge acquired from the state-of-the-art with respect to conducting DFR in an SDN platform addresses the knowledge gap of understanding these limitations.

Enhancing the security of Software-Defined Networks (SDNs)

Software-defined networking (SDN) is an emerging paradigm, which breaks the vertical integration in traditional networks to provide the flexibility to program the network through (logical) centralized network control. SDN has the capability to adapt its network parameters on the fly based on its operating environment. The decoupled structure of SDN serves as a solution for managing the network with more flexibility and ease. In SDN, the centralized cost effective architecture provides network visibility which helps to achieve efficient resource utilization and high performance. Due to the increasingly pervasive existence of smart programmable devices in the network, SDN provides security, and network virtualization for enhancing the overall network performance. The work presents various security threats that are resolved by SDN and new threats that arise as a result of SDN implementation. The recent security attacks and countermeasures in SDN are also summarized in the form of tables. Also provided a survey on the different strategies that are implemented to achieve energy efficiency and network security through SDN implementation. In an effort to anticipate the future evolution of this new paradigm, were discussed the main ongoing research efforts, challenges, and research trends in this area. With this work, researchers and students can have a more thorough understanding of SDN architecture, different security attacks and countermeasures.

A Secure Data Flow Forwarding Method Based on Service Ordering Management

The transmission of data flows in current networks is in a scattered and disordered state, which makes it difficult to effectively discover and defend against network attacks in a timely manner, while network managers lack the tools for the secure and orderly management of data flows. To solve this problem, a secure data flow forwarding method based on service ordering management is proposed in this paper. By defining the service header, the scheme realizes a fine-grained service-based division of data flows. The rules for services in the network are formulated, and orderly control over data flows based on the rules is implemented through the software-defined network architecture, such that only data flows meeting the rules are allowed to pass through the network. Meanwhile, to achieve secure data flow forwarding, data flow is signed, and the signature fields are sampled and verified on the forwarding device to ensure the correctness and tamperproof nature of the data flow forwarding process. The experimental results reveal that the proposed method based on service ordering management can achieve fine-grained and orderly secure data flow control forwarding, effectively defending against network attacks and improving network security. Furthermore, the additional forwarding delay introduced by the scheme is in the controllable range, making the approach practical.

Critical link identification algorithm for power communication networks in SDN architecture

With the maturity of software-defined network (SDN) technology, its application in power communication networks (PCNs) is being introduced. SDN controllers can assign working and backup routes for arriving serve requests and provide one-to-one (1:1) protection, which is crucial for the transmission of power system data with high reliability and delay requirements. For PCNs in SDN architecture, a critical link identification algorithm based on link-related risk (LRR-CLIA), which considers both working and backup routes between nodes, is proposed in this paper. The algorithm calculates link importance to identify critical links by quantifying the impact of links on the network risk on service layer, transport layer, and topology layer. To verify the effectiveness of the LRR-CLIA, we compare the network loss on service layer, transport layer, topology layer, and comprehensive layer with other algorithms after ranking and removing the identified critical links in descending order. In the simulation results, the LRR-CLIA outperforms the other algorithms by an average of 39.5% and 51.77% in the small PCN and medium-scale PCN respectively, which shows that the LRR-CLIA can identify the critical links more effectively and accurately in PCNs whose services have both working and backup paths.

Preventing Attacks on Wireless Networks Using SDN Controlled OODA Loops and Cyber Kill Chains

Impersonation-based attacks on wireless networks are easy to perform and can significantly impact network security. Their detection is problematic due to the attacks utilizing legitimate functions. This paper proposes a novel algorithm based on Observe-Orientate-Decide-Act (OODA) loop and Cyber Kill Chain (CKC) strategies to detect and neutralize these attacks. To evaluate this approach, we conducted experiments using four attack methods on a wireless router equivalent device, five wireless client devices, and two attack devices. The system employs a Radio Frequency (RF) device identification system and attack state machine implemented using a Software Defined Networking (SDN) architecture and the P4 programming language. The technique remains compliant with the IEEE 802.11 standard and requires no client-side modifications. The results show that the RF section detected 97.5% (average) of impersonated frames, and the overall method neutralized all attacks in the four attack scenarios. This outcome demonstrates that this technique, built on the OODA loops and CKC methodology, using SDN architecture and P4, is suitable for real-time detection and prevention of wireless impersonation attacks.

GOKA: A Network Partition and Cluster Fusion Algorithm for Controller Placement Problem in SDN

Software Defined Networking (SDN) is a new promising network architecture, with the property of decoupling the data plane from the control plane and centralizing the network topology logically, making the network more agile than traditional networks. However, with the continuous expansion of network scales, the single-controller SDN architecture is unable to meet the performance requirements of the network. As a result, the logically centralized and physically separated SDN multi-controller architecture comes into being, and thereupon the Controller Placement Problem (CPP) is proposed. In order to minimize the propagation latency in Wide Area Network (WAN), we propose Greedy Optimized K-means Algorithm (GOKA) which combines K-means with greedy algorithm. The main thought is to divide the network into multiple clusters, merge them greedily and iteratively until the given number of controllers is satisfied, and place a controller in each cluster through the K-means algorithm. With the purpose of proving the effectiveness of GOKA, we conduct experiments to compare with Pareto Simulated Annealing (PSA), Adaptive Bacterial Foraging Optimization (ABFO), K-means and K-means[Formula: see text] on 6 real topologies from the Internet Topology Zoo and Internet2 OS3E. The results demonstrate that GOKA has a better and more stable solution than other four heuristic algorithms, and can decrease the propagation latency by up to [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] in contrast to PSA, ABFO, K-means and K-means[Formula: see text], respectively. Moreover, the error rate between GOKA and the best solution is always less than [Formula: see text], which promises the precision of our proposed algorithm.

Empowering SDN-Docker Based Architecture for Internet of Things Heterogeneity

Internet of Things (IoT), an emerging technology, connects billions of smart devices to the Internet through heterogeneous communication technologies enabling novel services and applications. This complex and variable environment encounters several challenges such as managing and controlling the network and IoT applications, programming IoT devices, responding to some specific Quality of Service requirements of certain applications. The current IoT network based on the complex, static, and closed traditional architecture represents a serious concern, especially with the amounts of data generated from the huge and heterogeneous IoT devices. In this paper, we present a new model for IoT architecture that brings together the benefits of Software Defined Networking (SDN) architecture and containerization (i.e., Docker) to address most IoT challenges and abstract the underlying communication and hardware subsystem. The SDN concept decoupled the control plane from data plane which implies a centralized global network view and provides flexibility in network configuration and resource management. Moreover, containerization technique through the docker in the device brings the portability and scalability of the applications. First, we propose, an SDN-based architecture for IoT. Then, with lightweight virtualization of IoT device supplied by Docker, we empower the functionalities and effectiveness of the SDN based architecture solution to challenge the IoT heterogeneity. Furthermore, we use the smart supermarket as a use case to validate our SDN-Docker based architecture in a real IoT scenario. The experimental results validate the architecture implementation.

Intelligent SDN Architecture With Fuzzy Neural Network and Blockchain for Monitoring Critical Events

ABSTRACT The article deals with the creation of an intelligent architecture of the Internet of Things transport environment based on software-defined network (SDN) and blockchain for detecting threats and attacks. The transport environment is created for the monitoring system of critical events in the road transport infrastructure. Blockchain technology is used to authenticate network nodes, store sensor data in a distributed ledger. The network packet clustering method based on a fuzzy neural network is used to detect packets with possible malicious content. The intelligent SDN architecture is a hierarchy of four layers with six levels and includes: a) edge computing layer (sensor nodes and routers level, SDN switches data level), b) fog computing layer (zone server level, control level in SDN controllers), c) a cloud computing layer with data center servers, d) a layer for presenting monitoring results on user devices and applications. Detection of threats and attacks is implemented by validating network nodes and analyzing header fields of IP packets and TCP segments. The intrusion detection system includes a parser and analyzer of data packets, a module for filtering traffic by type, port numbers and other characteristics of packets, a module for synthesizing digital signatures of trusted nodes and their validation, a module for analyzing and clustering packets based on fuzzy logic and a neural network, modules for logging procedures. The probability function of packets belonging to clusters is tuned through deep learning of a five-layer neural network. The conclusion about belonging and degree of similarity with malicious packages is formed using the fuzzy logic apparatus. To train the neural network, the previously synthesized rules of the flow tables and the identified signs of atypical data packets are used. The functionality and effectiveness of the SDN architecture with an intrusion detection system is validated by simulating procedures in the NS3 Simulator system, evaluating authenticity, latency, throughput, response time, and accuracy in detecting atypical data packets.

A comprehensive survey on deep packet inspection for advanced network traffic analysis: issues and challenges

Deep Packet Inspection (DPI) provides full visibility into network traffic by performing detailed analysis on both packet header and packet payload. Accordingly, DPI has critical importance as it can be used in applications i.e network security or government surveillance. In this paper, we provide an extensive survey on DPI. Different from the previous studies, we try to efficiently integrate DPI techniques into network analysis mechanisms by identifying performance-limiting parameters in the analysis of modern network traffic. Analysis of the network traffic model with complex behaviors is carried out with powerful hybrid systems by combining more than one technique. Therefore, DPI methods are studied together with other techniques used in the analysis of network traffic. Security applications of DPI on Internet of Things (IoT) and Software-Defined Networking (SDN) architectures are discussed and Intrusion Detection Systems (IDS) mechanisms, in which the DPI is applied as a component of the hybrid system, are examined. In addition, methods that perform inspection of encrypted network traffic are emphasized and these methods are evaluated from the point of security, performance and functionality. Future research issues are also discussed taking into account the implementation challenges for all DPI processes.

Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network

The satellite-terrestrial integrated network (STIN), as an integration of the satellite network and terrestrial, has become a promising architecture to support global coverage and ubiquitous connection. The architecture of software-defined networking (SDN) is utilized to intelligently coordinate the global STIN, in which the placement schemes of SDN controllers, including the locations, number, and roles, would produce various performances. However, the uneven distribution of global users leads to the unbalanced energy consumption of satellite resources, which brings a heavy burden for satellites to maintain the control flows for network management. To provide green communication for international economic trade in the countries along the Belt and Road, in this paper, we focus on the energy-efficient controller placement (EECP) problem in the software-defined STIN. The satellite gateways are located in the countries along the Belt and Road, which accounts for a large number of traffic demands and a dense population. The controllers are deployed on the LEO satellites, where each LEO satellite is a candidate controller. The energy consumption for the control paths and the user data links is modeled and then formulated as the flow processing-oriented optimization problem. A modified simulated annealing placement (MSAP) algorithm is developed to solve the EECP problem, in which we use the greedy way to obtain the initial set of controllers, and then the final optimal controller placement result is obtained by the simulated annealing algorithm. Extensive simulations are conducted on the simulated Iridium satellite network topology and statistics data. Compared with other algorithms, the results show that MSAP reduces network energy consumption by 20% and average latency by 25%.

Edge Controller Placement for Next Generation Wireless Sensor Networks

Nowadays, Fog architecture or Edge architecture is becoming a popular research trend to distribute a substantial amount of computing resources, data processing and resource management at the extreme edge of the wireless sensor networks (WSNs). Industrial communication is a research track in next generation wireless sensor networks for the fourth revolution in the industrial process. Adopting fog architecture into Industrial communication systems is a promising technology within sensor networks architecture. With Software Defined Network (SDN) architecture, in this paper, we address edge controller placement as an optimization problem with the objective of more robustness while minimizing the delay of network management and the associated synchronization overhead. The optimization problem is provided and modelled as submodular function. Two algorithms are provided to find the optimal solution using a real wireless network to get more realistic results. Greedy Algorithm and Connectivity Ranking Algorithm are provided. Greedy algorithm outperforms connectivity ranking algorithm to find the optimum balance between the different metrics. Also, based on the network operator preference, the number of edge controllers to be placed will be provided. This research paper plays a great role in standardization of softwarization into Industrial communication systems for next generation wireless sensor networks.

LoRaWAN Network Downlink Routing Control Strategy Based on the SDN Framework and Improved ARIMA Model

In order to improve the downlink communication performance of the traditional LoRa wide area network (LoRaWAN), a LoRaWAN downlink routing control strategy based on the software defined networks (SDN) framework and the improved auto-regressive integrated moving average (ARIMA) model is proposed. The SDN architecture is used to monitor the network traffic, and the link bandwidth occupancy rate is calculated based on the monitored downlink traffic. Taking into account the impact of data volatility on the accuracy of the prediction results, the Savitzky–Golay (S–G) smoothing filter and the sliding window method are introduced for data pre-processing. Stationarity processing is carried out for the time series data in the window, and the ARIMA model is developed to predict the downlink bandwidth occupancy rate. The triangle module operator is then used to incorporate multiple path parameters to finally calculate the selectivity of different paths, and the optimal path for LoRaWAN downlink communication is then provided. Simulation and experimental results show that the root mean square error of the improved ARIMA prediction model is reduced by 87% compared with the standard ARIMA model. The proposed routing control strategy effectively reduces the service transmission delay and packet loss rate. In the LoRaWAN test environment, as the downlink load rate increases, the average link bandwidth occupancy rate of this solution increases by 12% compared with the traditional method.

An Energy-Efficient and Blockchain-Integrated Software Defined Network for the Industrial Internet of Things.

The number of unsecured and portable Internet of Things (IoT) devices in the smart industry is growing exponentially. A diversity of centralized and distributed platforms have been implemented to defend against security attacks; however, these platforms are insecure because of their low storage capacities, high power utilization, single node failure, underutilized resources, and high end-to-end delay. Blockchain and Software-Defined Networking (SDN) are growing technologies to create a secure system and to ensure safe network connectivity. Blockchain technology offers a strong and trustworthy foundation to deal with threats and problems, including safety, privacy, adaptability, scalability, and security. However, the integration of blockchain with SDN is still in the implementation phase, which provides an efficient resource allocation and reduced latency that can overcome the issues of industrial IoT networks. We propose an energy-efficient blockchain-integrated software-defined networking architecture for Industrial IoT (IIoT) to overcome these challenges. We present a framework for implementing decentralized blockchain integrated with SDN for IIoT applications to achieve efficient energy utilization and cluster-head selection. Additionally, the blockchain-enabled distributed ledger ensures data consistency throughout the SDN controller network and keeps a record of the nodes enforced in the controller. The simulation result shows that the proposed model provides the best energy consumption, end-to-end latency, and overall throughput compared to the existing works.

Enhance Software-Defined Network Security with IoT for Strengthen the Encryption of Information Access Control.

The Internet of Things (IoT) is legitimately growing quicker. The operators have already started setting up a diligent infrastructure for these gadgets. Various technologies need to be developed for this type of sensor, including enterprise safety initiatives. This paper covers the stability routing protocol, which assumes an assessment of credibility in gadgets and packet flow. To build reliable Software-Defined Network (SDN) routes, build on the trust between network element flows and Quality of Service (QoS) or energy conditions. The SDN architecture is used for the Cognitive Protocol Network (CPN) technical platform to increase the energy level. Stochastic Neural Networks (SNNs) are accredited with information extracted from perceptual packets and make decentralized decisions. The proposed network infrastructure is designed and integrated into the SerIoT techniques to strengthen IoT encryption for information access control. The versatility of the technology is to circumvent the unpredictable connectivity of the system and the node decreases in terms of potential cryptographic capacity, limited interval, a target node, and deterministic energy. Based on factual statistical data, appropriate marketing generates an end-to-end antitheft solution that meets a set of predetermined circuit restrictions. A study must collaborate by demonstrating numerous flaws due to the obvious instability of clusters, which is essential for the efficiency of the platform.

Controller placement problem in software defined networks

<span>The requirements <span>for the network are increasing by the expanding and spreading the Internet. The Previous techniques of the network do not meet the modern needs, thus, a new technical presents software-defined networking (SDN). SDN recognizes as a promising new model that separates the control plane (traffic routing and network topology) from the data plane (network architecture layers). The architecture of SDN has some features that find in a single controller or many controllers instance of programmable, flexible, and scalable. In the current SDN, multiple controllers are essential. Therefore, the optimal number of the controllers and their locations is the most significant challenge, known as the controller placement problem (CPP). It deploys the optimal number of controllers within the network while meeting presentations requirements considered conflicting in nature example: credibility, load balancing, latency, energy efficiency, and computation time. Many studies researched the ways to develop solutions for improving scalability, place selection for SDN. This paper presents the CPP and gives a comprehensive review of SDN issues based on the recent well-known research to extract available solution strategies. Finally, it discusses the limitations and future study directions that can support researchers in this field.</span></span>