Software Defined Network Environment Research Articles

Flooding distributed denial of service detection in software-defined networking using k-means and naïve Bayes

Software-defined networking (SDN) is a network architecture that enables the separation of the control plane and data plane, facilitating centralized management of the network. While centralized control offers numerous benefits, it also comes with certain drawbacks. Flooding distributed denial of service (DDoS) attacks pose a significant threat in SDN environments. These attacks involve overwhelming a target system with a large volume of packets, aiming to disrupt its functionality. In this paper, we propose a new approach for detecting DDoS attacks based on multiple k-means models and the naive Bayes algorithm. Our methodology involves training multiple k-means models to cluster each data point within every column of the dataset, where each column represents a feature. This process results in a new dataset with the same shape, containing only clusters, except the column containing the target variable (labels). These clusters are then used as input by naïve Bayes to perform binary classification. We assessed our approach using the InSDN and CIC-DDoS2017 datasets. The results underscore the impressive accuracy of our model, achieving 99.9839% on the InSDN dataset and 99.7030% on the CIC-DDoS2017 dataset. This performance was achieved by optimizing the desired number of clusters.

Leveraging SDN for scalable and sustainable fat tree networks: a multi-objective performance and energy efficiency evaluation of an 8-pod fat tree data center

Modern data centers increasingly rely on Software-Defined Networking (SDN) to address challenges related to scalability, performance, and efficient resource management. This research investigates the scalability and performance optimization of fat-tree topology within SDN environments, focusing on the impact of a sleep mode technique on network efficiency and energy consumption. Using the Mininet emulator, an 8-pod fat-tree network is simulated and compared against traditional routing methods like Equal-Cost Multi-Path (ECMP) and Dijkstra’s algorithm. The findings show that the sleep mode technique improves bandwidth utilization and throughput by reducing energy consumption during low-traffic periods without significantly affecting data flow. In contrast, Dijkstra’s algorithm exhibited reduced throughput due to inefficient path management, while ECMP did not fully optimize load balancing or energy efficiency. The sleep mode approach efficiently redistributes traffic across active switches, preventing congestion and outperforming both Dijkstra and ECMP in terms of average load. The results demonstrate that implementing sleep mode in fat-tree SDN networks enhances both network performance and energy efficiency, offering a practical solution for large-scale data center operations. These findings provide valuable insights for optimizing SDN-based traffic management in modern network infrastructures.

A Novel Space-Efficient Method for Detecting Network-Wide Heavy Hitters in Software-Defined Networking Using P4-Switch

Software-Defined Networking (SDN) is a dynamic, programmable approach that enables centralized control and has become essential in modern networking environments such as data centers, Internet Service Providers (ISPs), and emerging 5G applications. A critical challenge within SDN environments is detecting and managing “heavy hitters” high-traffic flows often associated with malicious activities like Distributed Denial of Service (DDoS) attacks or real-time data-intensive applications. Identifying these flows across multiple network switches is complex due to constraints like memory limitations and processing accuracy. This paper proposes a novel, network-wide solution for detecting Heavy Hitters (HH), moving beyond the single-switch approaches found in previous research. In contrast, the new strategy introduces two algorithms to enhance detection. The first algorithm leverages the P4 programming language to identify the local Top-k heavy flows at individual P4-enabled switches. The second algorithm employs dynamic thresholding to efficiently combine the Top-k lists from multiple switches, creating a centralized, coordinated network-wide detection system. The proposed system was rigorously tested in an SDN environment utilizing P4 switches. The results show that it achieves a high detection accuracy (95%-100%) while using only 10KB of memory per programmable switch. Furthermore, the approach outperforms existing state-of-the-art methods, providing higher accuracy and lower error rates with minimal memory usage

Reducing the Risk of Cyber Attack in SDN Network by using Blockchain

In the contemporary digital landscape, Software-Defined Networking (SDN) has emerged as a transformative approach that decouples network control from hardware, enabling greater flexibility and centralized management. However, this innovation has also introduced new vulnerabilities, making SDN networks susceptible to cyber-attacks. To reduce the risk of cyber-attack blockchain can play vital role. In paper we propose a novel framework that leverages blockchain technology to enhance the security and resilience of SDN environments against potential threats. By integrating blockchain’s decentralized and immutable characteristics, our approach facilitates secure data transactions, enhances network visibility, and fosters trust among network participants. We present a comprehensive analysis of the vulnerabilities inherent in traditional SDN architectures and outline method, how blockchain can mitigate these risks through secure authentication, data integrity, and enhanced access controls.Furthermore, we conduct a series of experiments to evaluate the performance impact and security benefits of our proposed solution. The results demonstrate a significant reduction in the likelihood of cyber-attacks, showcasing the viability of blockchain as a potent tool for safeguarding SDN networks. Our findings underscore the importance of interdisciplinary approaches in addressing the evolving challenges of network security, paving the way for more resilient and secure SDN infrastructures in the future.

Evaluation of supervised classification approach for DDoS threat detection in Software Defined Networks

Software defined networks (SDN) are the way of the future for networking, which separate the data and control planes of network devices to enable centralized control. SDN enhances network administration and security, allowing for programmability and improved performance. However, SDN is susceptible to attacks, particularly DDoS threats, which can overwhelm the network, restrict server access, and deplete network resources. This study offers a technique based on ML for recognizing DDoS threats in SDN. Various of ML models are assessed and trained using a publicly available SDN-specific dataset. These models’ performance is examined across different attack types, with results presented using parameters including F1_score, recall, precision and accuracy. The findings indicate the efficiency of ML techniques in identifying and mitigating against DDoS assaults in SDN environments.

Comprehensive Performance Evaluation of Machine Learning Algorithms for detecting DDoS attacks in SDN

<div align="center">Software-Defined Networking (SDN) revolutionizes networking by separating control logic and data forwarding, enhancing security against threats like Distributed Denial of Service (DDoS) attacks. These attacks flood control plane bandwidth, causing SDN network failures. Recent studies emphasize the efficacy of machine learning and statistical approaches in identifying and mitigating these security risks. However, there has been a lack of focus on employing ensembling techniques, amalgamating diverse machine learning models, selecting pertinent features, and utilizing oversampling techniques to balance categorical data. Our study evaluates 20 machine-learning models, emphasizing feature engineering and addressing class imbalance using Synthetic Minority Oversampling TEchnique (SMOTE). The results indicate that Ensemble methods such as LGBM Classifier, Random Forest Classifier, XGB Classifier, Decision Tree Classifier attained near-perfect scores (almost 100%) across all metrics, suggesting potential overfitting. Conversely, models like AdaBoost Classifier, K-Neighbors Classifier, and SVC exhibited slightly lower (99%) but realistic performance, underscoring the intricacy of accurate prediction in cybersecurity. Simpler models, including Logistic Regression, Linear Discriminant Analysis, and Gaussian Naive Bayes, demonstrated moderate to low accuracy, approximately around 70%. These findings stress the imperative need for a nuanced approach in the selection and fine-tuning of machine learning models to ensure effective DDoS detection in SDN environments.</div>

SDN-Driven Security Framework for DDOS Attack Detection and Mitigation

The rising complexity and persistence of Distributed Denial of Service (DDoS) attacks, particularly low-rate variants, present significant challenges in detection and mitigation within Software-Defined Networking (SDN) environments. Existing detection systems often flood networks with alerts, burdening security personnel and delaying timely mitigation. Furthermore, many solutions are designed and tested in simulated conditions, limiting their real-world applicability. To address these challenges, we propose an SDN-based security framework enhanced with automated monitoring, detection, and mitigation capabilities, optimized for slow-rate DDoS attacks. Our framework was rigorously evaluated on a physical testbed, achieving mitigation efficiencies between 91.66% and 100% under varied attack conditions, thus proving its robustness in practical settings. Additionally, we introduce the SDN-SlowRate-DDoS dataset, designed to assist researchers and industry professionals in developing and testing intrusion detection solutions in more realistic scenarios. To further improve DDoS defense in SDN, we incorporate an ensemble online machine learning model that dynamically adapts to evolving attack patterns, enhancing accuracy across attack types and outperforming traditional models with a detection rate of 99.2% on benchmark datasets. This dual approach leverages both real-world testing and adaptive machine learning, advancing proactive DDoS threat management in SDN environments

Intrusion detection in software defined network using deep learning approaches

Ensuring robust network security is crucial in the context of Software-Defined Networking(SDN). Which, becomes a multi-billion dollar industry, and it’s deployed in many data centers nowadays. The new technology provides network programmability, network centralized control, and a global view of the network. But, unfortunately, it comes with new vulnerabilities, and new attack vectors compared to the traditional network. SDN network cybersecurity became a trending research topic due to the hype of Machine Learning (ML) when a group of Machine Learning(ML) techniques called Deep Learning(DL) started to take shape in the setting of SDN networks. This paper focuses on developing advanced Deep Learning(DL) models to address the inherent new attack vectors. In this paper, we have built and compared two models that can be used for building a complete Intrusion Detection System(IDS) solution, one using a hybrid CNN-LSTM architecture and the other using Transformer encoder-only architecture. We specifically target the SDN controller where it represents a crucial point. We utilized the InSDN dataset for training and testing our models, this dataset captures real-world traffic within the SDN environment. For evaluation, we have used accuracy, precision, recall, and F1 Score. Our experiment results show that the Transformer model with 48 features achieves the highest accuracy at 99.02%, while the CNN-LSTM model achieves 99.01%. We have reduced the features to 6 and 4, which gave us varying impacts on the models’ performance. We have merged 4 poorly represented attacks in one class, which enhanced the accuracy by a significant score. Additionally, we investigate binary classification by merging all attack types into a single class, as a result, the accuracy increased for both models. The CNN-LSTM model achieves the best results with an accuracy of 99.19% for 6 feature sets, this enhances the state-of-the-art results.

Traffic-Driven Controller-Load-Balancing over Multi-Controller Software-Defined Networking Environment

Currently, more studies are focusing on traffic classification in software-defined networks (SDNs). Accurate classification and selecting the appropriate controller have benefited from the application of machine learning (ML) in practice. In this research, we study different classification models to see which one best classifies the generated dataset and goes on to be implemented for real-time classification. In our case, the classification and regression tree (CART) classifier produces the best classification results for the generated dataset, and logistic regression is also considerable. Based on the evaluation of various algorithmic outputs for the training and validation datasets, and also when execution time is taken into account, the CART is found to be the best algorithm. While testing the impact of load balancing in a multi-controller SDN environment, in different load case scenarios, we observe network performance parameters like bit rate, packet rate, and jitter. Here, the use of traffic classification-based load balancing improves the bit rate as well as the packet rate of traffic flow on a network and thus considerably enhances throughput. Finally, the reduction in jitter while increasing the controllers confirms the improvement in QoS in a balanced multi-controller SDN environment.

A Systematic Literature Review on Cyber Attack Detection in Software-Define Networking (SDN)

The increasing complexity and sophistication of cyberattacks pose significant challenges to traditional network security tools. Software-defined networking (SDN) has emerged as a promising solution because of its centralized management and adaptability. However, cyber-attack detection in SDN settings remains a vital issue. The current literature lacks comprehensive assessment of SDN cyber-attack detection methods including preparation techniques, benefits and types of attacks analysed in datasets. This gap hinders the understanding of the strengths and weaknesses of various detection approaches. This systematic literature review aims to examine SDN cyberattack detection, identify strengths, weaknesses, and gaps in existing techniques, and suggest future research directions in this critical area. A systematic approach was used to review and analyse various SDN cyberattack detection techniques from 2017--2024. A comprehensive assessment was conducted to address these research gaps and provide a comprehensive understanding of different detection methods. The study classified attacks on SDN planes, analysed detection datasets, discussed feature selection methods, evaluated approaches such as entropy, machine learning (ML), deep learning (DL), and federated learning (FL), and assessed metrics for evaluating defense mechanisms against cyberattacks. The review emphasized the importance of developing SDN-specific datasets and using advanced feature selection algorithms. It also provides valuable insights into the state-of-the-art techniques for detecting cyber-attacks in SDN and outlines a roadmap for future research in this critical area. This study identified research gaps and emphasized the importance of further exploration in specific areas to increase cybersecurity in SDN environments.

A novel Distributed Denial of Service attack defense scheme for Software-Defined Networking using Packet-In message and frequency domain analysis

Software-Defined Networking (SDN) enhances network management by improving adaptability, flexibility, and scalability. However, its centralized controller is vulnerable to Distributed Denial of Service (DDoS) attacks that can disrupt network availability. This study introduces a novel real-time DDoS detection scheme integrated into the SDN controller. The scheme uses a two-step process to analyze Packet-In messages in both time and frequency domains. A time-series is generated by sampling the number of Packet-In messages at specific time intervals, which is compared against a predefined threshold. If exceeded, frequency domain analysis is applied to extract features, which are then used by Machine Learning (ML) algorithms to identify DDoS attacks. The scheme achieves 99.85% accuracy in distinguishing normal traffic from attack traffic, demonstrating its effectiveness in safeguarding SDN environments from DDoS threats.

Deep learning approaches for protecting IoT devices in smart homes from MitM attacks

The primary objective of this paper is to enhance the security of IoT devices in Software-Defined Networking (SDN) environments against Man-in-the-Middle (MitM) attacks in smart homes using Artificial Intelligence (AI) methods as part of an Intrusion Detection and Prevention System (IDPS) framework. This framework aims to authenticate communication parties, ensure overall system and network security within SDN environments, and foster trust among users and stakeholders. The experimental analysis focuses on machine learning (ML) and deep learning (DL) algorithms, particularly those employed in Intrusion Detection Systems (IDS), such as Naive Bayes (NB), k-Nearest Neighbors (kNN), Random Forest (RF), and Convolutional Neural Networks (CNN). The CNN algorithm demonstrates exceptional performance on the training dataset, achieving 99.96% accuracy with minimal training time. It also shows favorable results in terms of detection speed, requiring only 1 s, and maintains a low False Alarm Rate (FAR) of 0.02%. Subsequently, the proposed framework was deployed in a testbed SDN environment to evaluate its detection capabilities across diverse network topologies, showcasing its efficiency compared to existing approaches.

SBAC-SDN: A Scalable Blockchain-based Access Control in Northbound Interface for Multi-Controller SDN with Load Balancing Mechanism

Abstract The rapid adoption of Software-Defined Networking (SDN) has not only revolutionized network management but also presented challenges in scalability and security, particularly in multi-controller environments. This paper introduces a load balancing technique aimed at enhancing the scalability of the Northbound interface, a critical component often susceptible to performance bottlenecks. The experimental setup utilized Mininet and Ryu to create a simulated multi-controller SDN environment, where SBAC-SDN was thoroughly evaluated based on CPU and memory usage, response times, and error rates. The results showed substantial improvements in critical performance indicators, confirming the effectiveness of the load balancing technique in addressing scalability challenges. The findings indicate that the system can be scaled without excessive resource consumption, as CPU usage peaked at 60.3% and memory usage remained below 8 GB even with eight controllers. The stable average response times were consistently below 1 s, and the high throughput, ranging from 4.34 to 4.67 requests per second, further demonstrates the efficiency of the system. The error rate remained 0.0% across all configurations, highlighting the robustness of the implemented security measures. This study contributes to ongoing efforts to improve the scalability and overall robustness of SDNs, aligning with the broader goals of reliability and efficiency in network management.

Innovative Machine Learning Strategies for DDoS Detection: A Review

This is a broad survey that investigates the use of machine learning (ML) methods for detecting distributed denial of service (DDoS) attacks. Traditional intrusion detection systems face difficulties in application-layer DDoS attacks because they target legal web traffic forms using standard transmission control protocol connections. This paper reviews different ML methods used in recent studies to tackle these issues. These studies use various data sets, such as UNSW-np-15, CICDDoS2019, and the novel dataset LATAM-DDoS-Internet of Things., which prove the efficacy of the proposed models in terms of accuracy and performance metrics. The second group of studies shows more advanced designs, such as protocol-based deep intrusion detection and autoencoder-multi-layer perceptron. These use deep learning to find features and group attacks. All of these approaches present favorable outcomes when it comes to distinguishing normal, DoS, and DDoS traffic with a high level of accuracy. Furthermore, the review discusses works that emphasize the early detection of noise-robust models and distributed frameworks. Different techniques, such as snake optimizer with ensemble learning, metastability theory, and spark-based anomaly detection, highlight the trend of predicting DDoS attacks, whereas hyperband-tuned deep neural networks and evolutionary support vector machine models show higher accuracy in cloud systems as well as software-defined networking environments. Hence, this review gives a general observation of how DDoS attacks develop on their way and proves that ML techniques help to strengthen network security.

An adaptive multistage intrusion detection and prevention system in software defined networking environment

The advancements made in Software-Defined Networking (SDN) technology seem quite promising, with potential wide application in managing and controlling the latest network infrastructures. SDN technology decouples the control plane from the data plane, enabling effective and flexible network management. However, this dynamic phenomenon brings new security challenges. With the increasing dynamism and programmable nature of networks, conventional security protocols may not sufficient to protect against advanced and sophisticated attacks. Although Intrusion Detection Systems (IDSs) have been extensively applied for identifying and preventing security threats in traditional network environments, IDS models designed specifically for traditional network requirements may not be adequate for SDN environments. These issues may stem from the static nature of conventional networks, contrasting with the dynamicity of advanced SDN networks, and the traditional IDS’s inability to adapt to the dynamic nature of SDN. To address these challenges, the current research proposes a novel Deep Hybrid IDS model to enhance network security in SDN environments and prevent attacks using Scapy. The proposed model detects signature-based attacks by integrating Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) for real-time simulated datasets, achieving an accuracy of 97.8%, which is comparatively better than existing models.

Machine Learning Models for DDoS Detection in Software-Defined Networking: A Comparative Analysis

In today's digital age, Software-Defined Networking (SDN) has become a pivotal technology that improves network control and flexibility. Despite its advantages, the centralized nature of SDN also makes it susceptible to threats such as Distributed Denial of Service (DDoS) attacks. This study compares the effectiveness of three machine learning models Random Forest, Naive Bayes, and Linear Support Vector Classification (LinearSVC) using the 'DDoS SDN dataset' from Kaggle, which contains 104,345 records and 23 features. An equal 70/30 ratio was used on model. The models were then assessed using measures such as accuracy, precision, recall, and F1-score, and ROC curves. Among the models, Random Forest outperformed the others with a 97% accuracy, precision values of 1.00 (benign traffic) and 0.94 (malicious traffic), and an ROC AUC score of 1.00. In contrast, Naive Bayes and LinearSVC recorded lower accuracies of 63% and 66%, respectively. These findings underscore Random Forest's effectiveness in detecting DDoS attacks within SDN environments.

An innovative NSGA-II-based Byzantine Fault Tolerant solution for software defined network environments

Byzantine fault tolerance (BFT) of the control plane in Software Defined Networking (SDN) is achieved by mapping each switch to 3f+1 number of controllers, where f represents the number of faulty controllers that can be tolerated at a time. A BFT approach protects the data plane from any potential malicious activity at the control plane by detecting the inconsistency among the response messages from multiple controllers. To compute the optimal mapping of switches to the controller, the existing literature does not consider some important parameters. This paper proposes a novel approach, named NBFT-SDN, that extends an artificial intelligence algorithm (i.e. NSGA-II) to solve a new formulated multi-objective optimization problem associated with this mapping. NBFT-SDN considers the very important parameters link reliability and link load along with switch-to-controller minimum delay, switch-to-controllers maximum reliability, controller-to-controller minimum delay, minimum link load, minimum hop count, and controller load balancing when mapping the switches to the controllers in optimum manner. The performance of our proposed approach is evaluated in comparison to a state-of-art approach using real network traces with network topologies of diverse sizes. Our proposed approach NBFT-SDN show improved network performance in terms of reliability, delay, hop count, load balancing and link load.

Traffic Classification in Software-Defined Networking Using Genetic Programming Tools

The classification of Software-Defined Networking (SDN) traffic is an essential tool for network management, network monitoring, traffic engineering, dynamic resource allocation planning, and applying Quality of Service (QoS) policies. The programmability nature of SDN, the holistic view of the network through SDN controllers, and the capability for dynamic adjustable and reconfigurable controllersare fertile ground for the development of new techniques for traffic classification. Although there are enough research works that have studied traffic classification methods in SDN environments, they have several shortcomings and gaps that need to be further investigated. In this study, we investigated traffic classification methods in SDN using publicly available SDN traffic trace datasets. We apply a series of classifiers, such as MLP (BFGS), FC2 (RBF), FC2 (MLP), Decision Tree, SVM, and GENCLASS, and evaluate their performance in terms of accuracy, detection rate, and precision. Of the methods used, GenClass appears to be more accurate in separating the categories of the problem than the rest, and this is reflected in both precision and recall. The key element of the GenClass method is that it can generate classification rules programmatically and detect the hidden associations that exist between the problem features and the desired classes. However, Genetic Programming-based techniques require significantly higher execution time compared to other machine learning techniques. This is most evident in the feature construction method where at each generation of the genetic algorithm, a set of learning models is required to be trained to evaluate the generated artificial features.

IOTASDN: IOTA 2.0 Smart Contracts for Securing Software-Defined Networking Ecosystem.

Software-Defined Networking (SDN) has revolutionized network management by providing unprecedented flexibility, control, and efficiency. However, its centralized architecture introduces critical security vulnerabilities. This paper introduces a novel approach to securing SDN environments using IOTA 2.0 smart contracts. The proposed system utilizes the IOTA Tangle, a directed acyclic graph (DAG) structure, to improve scalability and efficiency while eliminating transaction fees and reducing energy consumption. We introduce three smart contracts: Authority, Access Control, and DoS Detector, to ensure trusted and secure network operations, prevent unauthorized access, maintain the integrity of control data, and mitigate denial-of-service attacks. Through comprehensive simulations using Mininet and the ShimmerEVM IOTA Test Network, we demonstrate the efficacy of our approach in enhancing SDN security. Our findings highlight the potential of IOTA 2.0 smart contracts to provide a robust, decentralized solution for securing SDN environments, paving the way for the further integration of blockchain technologies in network management.

An entropy and machine learning based approach for DDoS attacks detection in software defined networks

Software-defined networks (SDNs) have been growing rapidly due to their ability to provide an efficient network management approach compared to traditional methods. However, one of the major challenges facing SDNs is the threat of Distributed Denial of Service (DDoS) attacks, which can severely impact network availability. Detecting and mitigating such attacks is challenging, given the constantly evolving range of attack techniques. In this paper, a novel hybrid approach is proposed that combines statistical methods with machine-learning capabilities to address the detection and mitigation of DDoS attacks in SDN environments. The statistical phase of the approach utilizes an entropy-based detection mechanism, while the machine-learning phase employs a clustering mechanism to analyze the impact of active users on the entropy of the system. The k-means algorithm is used for clustering. The proposed approach was experimentally evaluated using three modern datasets, namely, CIC-IDS2017, CSE-CIC-2018, and CICIDS2019. The results demonstrate the effectiveness of the system in detecting and blocking sudden and rapid attacks, highlighting the potential of the proposed approach to significantly enhance security against DDoS attacks in SDN environments.