Software Defined Network Environment Research Articles

Distributed Denial of Service Classification for Software-Defined Networking Using Grammatical Evolution

Software-Defined Networking (SDN) stands as a pivotal paradigm in network implementation, exerting a profound influence on the trajectory of technological advancement. The critical role of security within SDN cannot be overstated, with distributed denial of service (DDoS) emerging as a particularly disruptive threat, capable of causing large-scale disruptions. DDoS operates by generating malicious traffic that mimics normal network activity, leading to service disruptions. It becomes imperative to deploy mechanisms capable of distinguishing between benign and malicious traffic, serving as the initial line of defense against DDoS challenges. In addressing this concern, we propose the utilization of traffic classification as a foundational strategy for combatting DDoS. By categorizing traffic into malicious and normal streams, we establish a crucial first step in the development of effective DDoS mitigation strategies. The deleterious effects of DDoS extend to the point of potentially overwhelming networked servers, resulting in service failures and SDN server downtimes. To investigate and address this issue, our research employs a dataset encompassing both benign and malicious traffic within the SDN environment. A set of 23 features is harnessed for classification purposes, forming the basis for a comprehensive analysis and the development of robust defense mechanisms against DDoS in SDN. Initially, we compare GenClass with three common classification methods, namely the Bayes, K-Nearest Neighbours (KNN), and Random Forest methods. The proposed solution improves the average class error, demonstrating 6.58% error as opposed to the Bayes method error of 32.59%, KNN error of 18.45%, and Random Forest error of 30.70%. Moreover, we utilize classification procedures based on three methods based on grammatical evolution, which are applied to the aforementioned data. In particular, in terms of average class error, GenClass exhibits 6.58%, while NNC and FC2GEN exhibit average class errors of 12.51% and 15.86%, respectively.

Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment

In the contemporary landscape, Distributed Denial of Service (DDoS) attacks have emerged as an exceedingly pernicious threat, particularly in the context of network management centered around technologies like Software-Defined Networking (SDN). With the increasing intricacy and sophistication of DDoS attacks, the need for effective countermeasures has led to the adoption of Machine Learning (ML) techniques. Nevertheless, despite substantial advancements in this field, challenges persist, adversely affecting the accuracy of ML-based DDoS-detection systems. This article introduces a model designed to detect DDoS attacks. This model leverages a combination of Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) to enhance the performance of ML-based DDoS-detection systems within SDN environments. We propose utilizing the SHapley Additive exPlanations (SHAP) feature-selection technique and employing a Bayesian optimizer for hyperparameter tuning to optimize our model. To further solidify the relevance of our approach within SDN environments, we evaluate our model by using an open-source SDN dataset known as InSDN. Furthermore, we apply our model to the CICDDoS-2019 dataset. Our experimental results highlight a remarkable overall accuracy of 99.95% with CICDDoS-2019 and an impressive 99.98% accuracy with the InSDN dataset. These outcomes underscore the effectiveness of our proposed DDoS-detection model within SDN environments compared to existing techniques.

Reducing CPU Utilization & Improving Failover Time in Dual Controller SDN (Software Defined Network) Environment

A salient component of the network is failover. Largely, customers are accessing dual path controller software defined network (SDN) environment. Subsequently, when one controller fizzles out the network communication can persist as a result of the other controller. There will be no interruption in the network communication due to the failover setup which will be effective in case of failure of a controller. In the absence of failover, the network communication will be obstructed. This could be by cause of the system resources, system itself or routing policies or Distributed Denial of Services. Generally, controller will support many switches. When SDN controller collapses controller will not be in sync with switches. After few attempts switches will failover to the secondary SDN controller. During this shutdown time the network communication will be obstructed. Failover algorithm is the key to solve this issue and will facilitate the synchronization of the information with SDN controllers and enhance failover time. Apparently, communication between both SDN controllers and switches is enhanced as well as reinforces the time of synchronization due to multiple SDN controllers. CPU utilization is another problem in SDN environment as the new inflow packets need to be forwarded to control plane to find the action that need to be taken. Processing at both ends and having one to one mapping between new incoming connection requests and the packet from data plane to control plane increases the CPU utilization and there by more power consumption.

Developing an SDN security model (EnsureS) based on lightweight service path validation with batch hashing and tag verification

Software-defined networking (SDN) has significantly transformed the field of network management through the consolidation of control and provision of enhanced adaptability. However, this paradigm shift has concurrently presented novel security concerns. The preservation of service path integrity holds significant importance within SDN environments due to the potential for malevolent entities to exploit network flows, resulting in a range of security breaches. This research paper introduces a model called "EnsureS", which aims to enhance the security of SDN by proposing an efficient and secure service path validation approach. The proposed approach utilizes a Lightweight Service Path Validation using Batch Hashing and Tag Verification, focusing on improving service path validation's efficiency and security in SDN environments. The proposed EnsureS system utilizes two primary techniques in order to validate service pathways efficiently. Firstly, the method utilizes batch hashing in order to minimize computational overhead. The proposed EnsureS algorithm enhances performance by aggregating packets through batches rather than independently; the hashing process takes place on each one in the service pathway. Additionally, the implementation of tag verification enables network devices to efficiently verify the authenticity of packets by leveraging pre-established trust relationships. EnsureS provides a streamlined and effective approach for validating service paths in SDN environments by integrating these methodologies. In order to assess the efficacy of the Proposed EnsureS, a comprehensive series of investigations were conducted within a simulated SDN circumstance. The efficacy of Proposed EnsureS was then compared to that of established methods. The findings of our study indicate that the proposed EnsureS solution effectively minimizes computational overhead without compromising on the established security standards. The implementation successfully reduces the impact of different types of attacks, such as route alteration and packet spoofing, increasing SDN networks' general integrity.

Quality of Service Management Solution Becomes a Software-Defined Network Challenge

The use of Software-Defined Networking (SDN) has created a huge leap in the management of computer networks. SDN offers flexibility and the ability to dynamically manage networks, but along with its advantages, it also brings significant challenges in managing quality of service (QoS). QoS is critical to maintaining performance and user experience in increasingly complex and distributed network environments. The research method uses Solution Development to address a quality of service (QoS) management challenge and solution in Software Defined Networking with the approach of designing and developing practical solutions to address QoS management issues and challenges in SDN environments. Control Separation and Data Plane are centralized network control at the SDN controller, while the data plane reside in the networks hardware, both capable of maintaining QoS and a challenges in SDN with a computer network approach that separates the control layer from the data layer, enabling more flexible and centralized network management. Research conclusions related to SDN Software Defined Networking quality of service management problems and solutions, focusing on the problem of separating management and data levels. The separations of control and data plane concept in SDN has great potential to improve flexibility, scalability and more efficient network management.

AI-Enabled Traffic Control Prioritization in Software-Defined IoT Networks for Smart Agriculture.

Smart agricultural systems have received a great deal of interest in recent years because of their potential for improving the efficiency and productivity of farming practices. These systems gather and analyze environmental data such as temperature, soil moisture, humidity, etc., using sensor networks and Internet of Things (IoT) devices. This information can then be utilized to improve crop growth, identify plant illnesses, and minimize water usage. However, dealing with data complexity and dynamism can be difficult when using traditional processing methods. As a solution to this, we offer a novel framework that combines Machine Learning (ML) with a Reinforcement Learning (RL) algorithm to optimize traffic routing inside Software-Defined Networks (SDN) through traffic classifications. ML models such as Logistic Regression (LR), Random Forest (RF), k-nearest Neighbours (KNN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Trees (DT) are used to categorize data traffic into emergency, normal, and on-demand. The basic version of RL, i.e., the Q-learning (QL) algorithm, is utilized alongside the SDN paradigm to optimize routing based on traffic classes. It is worth mentioning that RF and DT outperform the other ML models in terms of accuracy. Our results illustrate the importance of the suggested technique in optimizing traffic routing in SDN environments. Integrating ML-based data classification with the QL method improves resource allocation, reduces latency, and improves the delivery of emergency traffic. The versatility of SDN facilitates the adaption of routing algorithms depending on real-time changes in network circumstances and traffic characteristics.

A reinforcement learning approach for widest path routing in software-defined networks

In this paper, a routing method based on reinforcement learning (RL) under software-defined networks (SDN), namely the Q-learning widest-path routing algorithm (Q-WPRA), is proposed. This algorithm processes the reward function according to the link bandwidth in the execution environment to find the optimal (i.e., widest) transmission path with the maximum bandwidth between the source and the destination through RL. The experimental results reveal that the Q-WPRA is outperformance than Dijkstra’s algorithm and Dijkstra’s widest-path algorithm to find the widest transmission path in SDN environment under different bandwidths, loss rates, and background traffic.

An Improved Deep Learning Model for DDoS Detection Based on Hybrid Stacked Autoencoder and Checkpoint Network

The software defined network (SDN) collects network traffic data and proactively manages networks. SDN’s programmability makes it excellent for developing distributed applications, cybersecurity, and decentralized network control in multitenant data centers. This exceptional architecture is vulnerable to security concerns, such as distributed denial of service (DDoS) attacks. DDoS attacks can be very serious due to the fact that they prevent authentic users from accessing, temporarily or indefinitely, resources they would normally expect to have. Moreover, there are continuous efforts from attackers to produce new techniques to avoid detection. Furthermore, many existing DDoS detection methods now in use have a high potential for producing false positives. This motivates us to provide an overview of the research studies that have already been conducted in this area and point out the strengths and weaknesses of each of those approaches. Hence, adopting an optimal detection method is necessary to overcome these issues. Thus, it is crucial to accurately detect abnormal flows to maintain the availability and security of the network. In this work, we propose hybrid deep learning algorithms, which are the long short-term memory network (LSTM) and convolutional neural network (CNN) with a stack autoencoder for DDoS attack detection and checkpoint network, which is a fault tolerance strategy for long-running processes. The proposed approach is trained and tested with the aid of two DDoS attack datasets in the SDN environment: the DDoS attack SDN dataset and Botnet dataset. The results show that the proposed model achieves a very high accuracy, reaching 99.99% in training, 99.92% in validation, and 100% in precision, recall, and F1 score with the DDoS attack SDN dataset. Also, it achieves 100% in all metrics with the Botnet dataset. Experimental results reveal that our proposed model has a high feature extraction ability and high performance in detecting attacks. All performance metrics indicate that the proposed approach is appropriate for a real-world flow detection environment.

Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey

Digital twins and the Internet of Things (IoT) have gained significant research attention in recent years due to their potential advantages in various domains, and vehicular ad hoc networks (VANETs) are one such application. VANETs can provide a wide range of services for passengers and drivers, including safety, convenience, and information. The dynamic nature of these environments poses several challenges, including intermittent connectivity, quality of service (QoS), and heterogeneous applications. Combining intelligent technologies and software-defined networking (SDN) with VANETs (termed intelligent software-defined vehicular networks (iSDVNs)) meets these challenges. In this context, several types of research have been published, and we summarize their benefits and limitations. We also aim to survey stochastic modeling and performance analysis for iSDVNs and the uses of machine-learning algorithms through digital twin networks (DTNs), which are also part of iSDVNs. We first present a taxonomy of SDVN architectures based on their modes of operation. Next, we survey and classify the state-of-the-art iSDVN routing protocols, stochastic computations, and resource allocations. The evolution of SDN causes its complexity to increase, posing a significant challenge to efficient network management. Digital twins offer a promising solution to address these challenges. This paper explores the relationship between digital twins and SDN and also proposes a novel approach to improve network management in SDN environments by increasing digital twin capabilities. We analyze the pitfalls of these state-of-the-art iSDVN protocols and compare them using tables. Finally, we summarize several challenges faced by current iSDVNs and possible future directions to make iSDVNs autonomous.

Detection and Mitigation of (D)DoS Attacks in SDN Environment Using Entropy

Software Defined Networking (SDN) is a paradigm for the networks, where the control planes and data planes are separated. It provides centralized network control by separating the network’s control logic from the underlying hardware devices. However, like traditional networks SDN is also susceptible to Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. This paper aims to detect and mitigate DoS and DDoS attacks in an SDN environment using an entropy-based approach. The proposed mechanism calculates the entropy of the network over the collected traffic, and derives a dynamic threshold according to the network traffic conditions to determine whether the environment is subject to DoS or DDoS attacks. In the event of the attack, the proposed mechanism installs a drop flow rule into underlying forwarding devices, discarding the traffic sent from attacking host to victim host

DDoS Attack Detection and Classification Using Hybrid Model for Multicontroller SDN

A software-defined network (SDN) brings a lot of advantages to the world of networking through flexibility and centralized management; however, this centralized control makes it susceptible to different types of attacks. Distributed denial of service (DDoS) is one of the most dangerous attacks that are frequently launched against the controller to put it out of service. This work takes the special ability of SDN to propose a solution that is an implementation run at the multicontroller to detect a DDoS attack at the early stage. This method not only detects the attacks but also identifies the attacking paths and starts a mitigation process to provide protection for the network devices. This method is based on the entropy variation of the destination host targeted with its IP address and can detect the attack within the first 250 packets of malicious traffic attacking a particular host. Then, fine-grained packet-based detection is performed using a deep-learning model to classify the attack into different types of attack categories. Lastly, the controller sends the updated traffic information to neighbor controllers. The chi-squared ( x 2 ) test feature selection algorithm was also employed to reveal the most relevant features that scored the highest in the provided data set. The experiment result demonstrated that the proposed Long Short-Term Memory (LSTM) model achieved an accuracy of up to 99.42% using the data set CICDDoS2019, which has the potential to detect and classify the DDoS attack traffic effectively in the multicontroller SDN environment. In this regard, it has an enhanced accuracy level to 0.42% compared with the RNN-AE model with data set CICDDoS2019, while it has improved up to 0.44% in comparison with the CNN model with the different data set ICICDDoS2017.

Applying Transfer Learning Approaches for Intrusion Detection in Software-Defined Networking

In traditional network management, the configuration of routing policies and associated settings on individual routers and switches was performed manually, incurring a considerable cost. By centralizing network management, software-defined networking (SDN) technology has reduced hardware construction costs and increased flexibility. However, this centralized architecture renders information security vulnerable to network attacks, making intrusion detection in the SDN environment crucial. Machine-learning approaches have been widely used for intrusion detection recently. However, critical issues such as unknown attacks, insufficient data, and class imbalance may significantly affect the performance of typical machine learning. We addressed these problems and proposed a transfer-learning method based on the SDN environment. The following experimental results showed that our method outperforms typical machine learning methods. (1) our model achieved a F1-score of 0.71 for anomaly detection for unknown attacks; (2) for small samples, our model achieved a F1-score of 0.98 for anomaly detection and a F1-score of 0.51 for attack types identification; (3) for class imbalance, our model achieved an F1-score of 1.00 for anomaly detection and 0.91 for attack type identification. In addition, our model required 15,230 seconds (4 h 13 m 50 s) for training, ranking second among the six models when considering both performance and efficiency. In future studies, we plan to combine sampling techniques with few-shot learning to improve the performance of minority classes in class imbalance scenarios.

Detection of DDOS Attack using Decision Tree Classifier in SDN Environment

Software Defined Networking (SDN) is a dynamic architecture that employs a variety of applications for making networks more adaptable and centrally controlled. It is easy to attack the entire network in SDN because the control plane and data plane are separated. DDoS attack is major danger to SDN service providers because it can shut down the entire network and stop services to all customers at any time. One of the key flaws of most SDN architectures is lack of susceptibility to DDoS attacks with its types like TCP flooding, UDP flooding, SYN flooding, ICMP flooding and DHCP flooding for detecting those kinds of attacks. The machine learning algorithms are widely used in recent years to identify DDoS attacks. This research utilizes Decision Tree Classifier for detection and classification of DDoS attacks on SDN. The Forward Feature Selection technique is also used in the research to select the best features from the dataset and from that dataset the data are employed to train and test the model by Decision Tree Classifier Algorithm. The decision Tree Classifier technique is a supervised method used to forecast desired values of observations using rudimentary machine learning decision rules derived from training data. Based on the accuracy of decision tree techniques, in future, a hybrid learning model will be designed for detecting the Distributed Denial of Services in an SDN environment with high accuracy and a low false negative rate.

Design of an optimized traffic‐aware routing algorithm using integer linear programming for software‐defined networking

SummaryThe number of internet users and connected devices has dramatically expanded due to the recent technological boom and the benefits that the internet of things offers to ease our lives. Network scheduling, quality of service, resource allocation, and security issues are now being addressed via software‐defined networking (SDN). SDN has several benefits over traditional networks, including global centralized control, managing network traffic, and separating the forwarding and control plane. The work done in this paper aims to design and implement a traffic‐aware routing framework based on routing optimization presented as an integer linear programming (ILP) to improve heterogeneous traffic flows' quality of service (QoS) in a simulated SDN environment. With the knowledge that the routing problem is a nondeterministic polynomial‐time‐hard problem, the proposed scheme aims to decrease the computational routing time to make the ILP‐based routing system more suitable for real‐time processing. The simulation results illustrate that the proposed framework reduces the computational time by 23% and 49% for Abilene and Goodnet topology, respectively. Additionally, with 1000 flows in the network, the suggested scheme reduces the number of network flows that violate the QoS by 9% and 22% (with Abilene topology) and 16% and 51% (with Goodnet topology) as compared to the existing shortest path delay and sway methods, respectively.

Performance and Scalability Analysis of SDN-Based Large-Scale Wi-Fi Networks

The Software-Defined Networking (SDN) paradigm is one that is utilized frequently in data centers. Software-Defined Wireless Networking, often known as SDWN, refers to an environment in which concepts from SDN are implemented in wireless networks. The SDWN is struggling with challenges of scalability and performance as a result of the growing number of wireless networks in its coverage area. It is thought that SDN techniques, such as Mininet-Wi-Fi and Ryu Controller for wireless networks, can overcome the problems with scalability and performance. Existing Wi-Fi systems do not provide SDN execution to end clients, which is one reason why the capability of Wi-Fi is restricted on SDN architecture. Within the scope of this study, we analyzed Wi-Fi networks operating on SDN using the Transmission Control Protocol (TCP) and User Datagram Protocol (UDP). By utilizing a testbed consisting of Ryu Controller and Mininet-Wi-Fi, we were able to test Wi-Fi over SDN and evaluate its performance in addition to its scalability. When evaluating the performance of a network, we take into account a number of different metrics, such as bandwidth, round-trip time, and jitter. In order to assess the level of performance, the SDN-based Wi-Fi controller Ryu is linked to an increasing number of access points (1, 2, 3, and 4) as well as stations (10, 30, 50, and 100). The experimental findings obtained using Mininet-Wi-Fi indicate the scalability and dependability of the network performance provided by the SDN Wi-Fi network controller Ryu in an SDN environment. In addition, the round-trip time for TCP packets grows proportionally with the number of hops involved. A single access point is capable of simultaneously supporting up to fifty people at once.

Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an SVM-based DDoS detection model shows superior performance. This comparative analysis offers a valuable insight into the development of efficient and accurate techniques for detecting DDoS attacks in SDN environments with less complexity and time.

A Fuzzy Based Routing Approach for Improving Online Conferencing Services in Software Defined Networking

In recent years, the provision of applications based on conferencing services over Software-Defined Networking (SDN) is increasing. So far, extensive studies have been conducted on improving conferencing services and supporting high-quality communication in SDN, and the importance of the issue has been understood by the research society. One of the challenges of guaranteeing conferencing services in SDN is the routing of network traffic taking into account Quality of Service (QoS) requirements. Most routing techniques in SDN guarantee end-to-end delay and bandwidth constraints. However, resource loss also plays a role in network performance, where it is clearly neglected in existing works. This study presents a guaranteed Latency-Bandwidth-Loss Routing algorithm based on Fuzzy approach to improve the performance of conference services in SDN (LBLRF). The purpose of the fuzzy-based approach is to increase the number of routed requests and improve the performance of conference services in SDN. LBLRF uses a postponement mechanism to improve the conference service, where it gives higher priority to the requests with low resource demand for connection. In addition, LBLRF is equipped with a hold time mechanism to release the reserved resources after satisfying the request requirements. This mechanism can increase the processing capacity of future requests by conserving network resources. Evaluations have been done by defining several scenarios on a synthetic SDN environment. The results of the experiments show that LBLRF provides a better request acceptance rate between 7% and 16% compared to the best results of existing algorithms.

Network Situation Assessment Method Based on Improved BP Neural Network

Although a software defined network (SDN) realizes the flexible configuration and centralized control of network resources, there are potential security risks and challenges. Network security situation awareness (NSSA) technology associates and integrates multi-source heterogeneous information to analyze the impact of the information on the whole network, and network security situation assessment can grasp the network security situation information in real time. However, the existing situation assessment methods have low assessment accuracy, and most of the studies focus on traditional networks, while there are few situation assessment studies in the SDN environment. In this paper, by summarizing the important index parameters of SDN, a network security situation assessment model based on the improved back propagation (BP) neural network (based on the cuckoo search algorithm) is proposed, and the step factor of the cuckoo search algorithm (CS) was improved to improve the search accuracy. The model maps the situation elements to the layers of the neural network, and optimizes the weights and thresholds of the BP neural network through the cuckoo search algorithm to obtain the global optimal solution; it finally realizes the purpose of situation assessment and the comprehensive rating of the SDN environment. In this paper, the evaluation model was verified on the network set up in Mininet. The experimental results show that the situation assessment curve of this model is closer to the real situation value, and the accuracy rate is 97.61%, with good situation assessment results.

Analytical study of effects of DDoS mitigation methods

Distributed Denial of Service (DDoS) attacks continue to pose a significant threat to network infrastructure and services. Software Defined Networks (SDNs) have emerged as a promising technology to enhance network flexibility and adaptability. This paper explores various DDoS attack mitigation approaches within the context of SDNs, with a focus on their effectiveness, limitations, and key considerations. The study begins by providing an overview of DDoS attacks and their evolving complexities. It then delves into the fundamental principles of SDNs and their potential advantages in combating DDoS attacks. Various mitigation techniques are analyzed, including rate limiting, flow diversion, and traffic anomaly detection, and their applicability in an SDN environment is discussed Our research takes a multi-dimensional approach, evaluating the performance of mitigation methods in terms of attack detection, response time, false positives, and the impact on legitimate traffic. Additionally, the study considers the adaptability of these methods to emerging threats, such as IoT-based attacks and zero-day vulnerabilities. Furthermore, we discuss the trade-offs and challenges associated with the implementation of these methods, including their impact on user experience, resource utilization, and operational overhead. The results of this comparative analysis aim to provide valuable insights for organizations and network security professionals in selecting appropriate DDoS mitigation strategies. This paper provides insights into the evolving landscape of DDoS attack mitigation in Software Defined Networks, emphasizing the need for a proactive and dynamic defence strategy to protect network infrastructure and ensure uninterrupted service availability. This study serves as a valuable resource for mitigating the risks and consequences of DDoS attacks in today's interconnected and digitally-dependent world.

Anomaly Detection in Network Traffic using Machine Learning for Early Threat Detection

Due to advances in network technologies, the amount of people using networks is rising rapidly. This has resulted in a large amount of transmission information being generated and moved across the network. However, this data is vulnerable to attacks and intrusions. To prevent network intrusions, security measures must be implemented, which can detect anomalies and identify potential threats. Network security researchers and labs have done extensive research in network security. The purpose of this study was to perform a noninvasive inspection to give a large general mechanism on recent advances in abnormality detection. The study reviewed recent research published in the past five years, which examined new technologies and potential future opportunities in anomaly detection. The literature review focused specifically on anomaly detection systems used in network traffic. This included various applications such as Wireless Sensor Networks (WSN), Internet of Things (IoT), High Performance Computing, Industrial Control Systems (ICS), and Software Defined Networking (SDN) environments. The review concludes by highlighting several unresolved issues that need to be addressed in order to improve anomaly detection systems.