Software Defined Networking Security Research Articles

Exploring LDoS Attack Detection in SDNs using Machine Learning Techniques

This study investigates the application of machine learning algorithms for detecting Low-Rate Denial-of-Service (LDoS) attacks within Software-Defined Networks (SDNs). LDoS attacks are challenging to detect due to their similarity to normal network behavior. This study evaluates the performance of algorithms such as Logistic Regression (LR), K-Nearest Neighbors (KNN), and BIRCH clustering in this challenge. The results show that the LR and BIRCH algorithms outperformed other approaches, achieving a detection accuracy of 99.96% with minimal false positive and negative rates. The models demonstrated a fast detection time of 0.03 seconds, highlighting the potential of machine learning to improve SDN security. The study recommends future work to validate these findings in real-world environments to strengthen security systems.

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.

When SDN Meets Low-rate Threats: A Survey of Attacks and Countermeasures in Programmable Networks

Low-rate threats are a class of attack vectors that are disruptive and stealthy, typically crafted for security vulnerabilities. They have been the significant concern for cyber security, impacting both conventional IP-based networks and emerging Software-Defined Networking (SDN). SDN is a revolutionary architecture that separates the control and data planes, offering advantages such as enhanced manageability, flexibility, and network programmability, as well as the ability to introduce new solutions to address security threats. However, its innovative design also poses new vulnerabilities and threats, especially susceptibility to low-rate threats. To this end, this paper presents a comprehensive overview of low-rate threats in programmable networks. It explores low-rate threats and countermeasures within the SDN architecture, encompassing the data plane, control plane, control channel, and application plane, together with traditional low-rate threats and countermeasures in SDN. Furthermore, the paper offers detailed insight into threats and countermeasures against low-rate attacks exploiting SDN vulnerabilities and low-rate attacks related to Programmable Data Plane (PDP). Additionally, it presents a comparative analysis and discussion of low-rate attacks versus high-volume attacks, along with suggestions for enhancing SDN security. This thorough review aims to assist researchers in developing more resilient and dependable countermeasures against low-rate threats in programmable networks.

Hybrid Deep Learning Approach for Enhanced Detection and Mitigation of DDOS Attack in SDN Networks

The pervasiveness of (DDoS) Distributed Denial of Service attacks has intensified the demand for effective and dependable detection methods in Software-Defined Networks (SDNs). This proposed study introduces a hybrid Deep Learning framework designed to identify and address DDoS attacks in Software-Defined Networking (SDN) contexts. Due to the centralization of SDN control planes, these networks are especially susceptible to DDoS attacks, which can saturate system resources and disrupt critical services. Utilizing the CICDDoS2019 dataset, this research integrates Recurrent Neural Networks (RNN), Deep Belief Networks (DBN), and Adaptive Feature Dimensionality Learning (AFDL) to improve detection accuracy and efficiency. In order to appropriately differentiate between attack and normal traffic, the proposed hybrid model combines both temporal dependencies and feature correlations achieving an accuracy of 99.80%. This research enhances SDN security by offering a scalable and reliable DDoS detection solution capable of adapting to real-time network requirements, addressing the prospective of DL in protecting network infrastructures.

Research on neural networks in computer network security evaluation and prediction methods

Anomaly detection in networks to identify intrusions is a common and successful security measure used in many different types of network infrastructure. Network data traffic has increased due to the proliferation of viruses and other forms of cyber-attacks as network technology and applications have developed quickly. The limitations of classical intrusion detection, such as poor detection accuracy, high false negatives, and dependence on dimensionality reduction methods, become more apparent in the face of massive traffic volumes and characteristic information. That’s why IoT infrastructures often use Software-Defined Networking (SDN), allowing for better network adaptability and control. Hence, this paper’s convolutional neural network-based Security Evaluation Model (CNN-SEM) is proposed to secure the source SDN controller from traffic degradation and protect the source network from DDoS assaults. The proposed CNN-SEM system might defend against DDoS assaults once discovered by applying and testing a Convolutional Neural Network (CNN). The model can automatically extract the useful aspects of incursion samples, allowing for precise classification of such data. The detection and mitigation modules evaluate the proposed SDN security system’s performance, and the findings showed promise against next-generation DDoS assaults. The experimental results show the CNN-SEM achieves a high accuracy ratio of 96.6%, a detection ratio of 97.1%, precision ratio of 97.2%, a performance ratio of 95.1% and an enhanced security rate of 98.1% compared to other methods.

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.

Evaluation of SDN security measures in the context of IEC 62443-3-3

The security of assets within electrical substations is paramount to ensuring the reliable and resilient operation of the energy sector. However, implementing existing industry cybersecurity standards in these environments presents numerous technical challenges. In this work, we provide systematic guidance that emphasizes best practices and prioritizes requirement implementation. We examine the application of Software-Defined Networking (SDN) as a means to enhance security within the IEC 62443 family of standards. Specifically, we offer insights into how the security measures required for compliance with the IEC 62443 security standards can impact the stringent timing constraints of contemporary communication protocols, enabling advanced distribution system operations in the future. Utilizing a testbed modeled after a real-world electrical substation, we demonstrate that while SDN-based security features naturally introduce some additional latency, their operational impact on the network’s strict constraints is minimal.

COMPARISON OF MACHINE LEARNING TECHNIQUES FOR CLASSIFICATION OF DISTRIBUTED DENIAL OF SERVICE ATTACKS BASED ON FEATURE ENGINEERING IN SDN-BASED NETWORKS

Distributed Denial-of-Service (DDoS) attacks present a noteworthy cybersecurity hazard to software-defined networks (SDNs). This investigation presents an approach that depends on feature engineering and machine learning to discern DDoS attacks in SDNs. Initially, the dataset acquired from Kaggle goes through cleansing and normalization procedures, and the optimal subset of features is determined by employing the Correlation-based Feature Selection (CFS) algorithm. Subsequently, the optimal subset of features is trained and evaluated utilizing diverse Machine Learning algorithms, specifically Random Forest (RF), Decision Tree, Adaptive Boosting (AdaBoost), K-Nearest Neighbor (k-NN), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). The outcomes demonstrate that XGBoost outperforms the other algorithms in various performance metrics (e.g., accuracy, precision, recall, F1, and AUC values). Furthermore, a comparative analysis was carried out among various models and algorithms, revealing that the technique proposed by the researchers yielded the most favourable outcomes and effectively detected and identified DDoS attacks in SDN. Consequently, this investigation provides a novel perspective and resolution for SDN security.

BSSN-SDNs: A Blockchain-Based Security Service Negotiation for the SDN Interdomain

The security requirements for SDN (Software-Defined Network) cross-domain communication are diverse and dynamically changing; thus, a security service negotiation function is required for the SDN interdomain. However, the SDN interdomain distributed communication environment leads to a lack of trustworthiness and security. Therefore, this paper proposes a blockchain-based SDN interdomain security service negotiation mechanism, BSSN-SDNs, to provide automatic, secure, and trustworthy SDN interdomain security service negotiation. BSSN-SDNs proposes a three-layer reference architecture that enables joint on-chain and off-chain work by extending the security service negotiation module and blockchain client on the controller and deploying security service negotiation smart contracts on the blockchain. It especially adopts non-interactive key exchange and the message authentication code to ensure the confidentiality of the secure service negotiated on-chain. Finally, the timeliness as well as security and trustworthiness of BSSN-SDNs are analyzed, and the FISCO BCOS-based experiment results show that the delay of BSSN-SDNs is acceptable and is positively correlated with the number of policies and the number of SDN domains involved in negotiation.

Distributed SDN Security Frameworks: A Survey

Software-Defined Networks (SDN) have recently received much attention and deployment as a new technology that offers more flexibility and efficiency than traditional networks. SDN is a paradigm shift that revolutionizes traditional network design and makes future networks programmable, manageable, and affordable. The use of the SDN in modern networks provides much needed flexibility and transparency to organize and deploy network solutions. It is a new model that separates forwarding & controlling planes, and centralized architecture designed to increase network speed and programming capability. However, from the current security point of view, the SDN still has some problems, especially for the advanced persistent attacks such as the DDOS, the side channel attacks in Clouds, the SDN stack control plane saturation attacks, and the switch flow table exhaustion attacks. Also, the existing SDN-based security systems are constrained by a central framework that provides significant overheads for the control plane, leading to the breakdown of vital control links. In this paper, we will present the vulnerabilities and security threats in the SDN network, define the various approaches to solve these problems, and deploy the SDN securely in the production environments. We will survey existing research on distributed SDN security frameworks, there is a number of security frameworks and applications that have been proposed previously, and each one of them builds on a selection of the SDN characteristics.

A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking

The ever-increasing diversity of Internet applications and the rapid evolution of network infrastructure due to emerging technologies have made network management more challenging. Effective traffic classification is critical for efficiently managing network resources and aligning with service quality and security demands. The centralized controller of software-defined networking provides a comprehensive network view, simplifying traffic analysis and offering direct programmability features. When combined with deep learning techniques, these characteristics enable the incorporation of intelligence into networks, leading to optimization and improved network management and maintenance. Therefore, this research aims to develop a model for traffic classification by application types and network attacks using deep learning techniques to enhance the quality of service and security in software-defined networking. The SEMMA method is employed to deploy the model, and the classifiers are trained with four algorithms, namely LSTM, BiLSTM, GRU, and BiGRU, using selected features from two public datasets. These results underscore the remarkable effectiveness of the GRU model in traffic classification. Hence, the outcomes achieved in this research surpass state-of-the-art methods and showcase the effectiveness of a deep learning model within a traffic classification in an SDN environment.

TITAN: Combining a bidirectional forwarding graph and GCN to detect saturation attack targeted at SDN.

The decoupling of control and forwarding layers brings Software-Defined Networking (SDN) the network programmability and global control capability, but it also poses SDN security risks. The adversaries can use the forwarding and control decoupling character of SDN to forge legitimate traffic, launching saturation attacks targeted at SDN switches. These attacks can cause the overflow of switch flow tables, thus making the switch cannot forward benign network traffic. How to effectively detect saturation attack is a research hotspot. There are only a few graph-based saturation attack detection methods. Meanwhile, the current graph generation methods may take useless or misleading information to the attack detection, thus decreasing the attack detection accuracy. To solve the above problems, this paper proposes TITAN, a bidirecTional forwardIng graph-based saturaTion Attack detectioN method. TITAN defines flow forwarding rules and topology information, and designs flow statistical features. Based on these definitions, TITAN generates nodes of the bi-forwarding graph based on the flow statistics features and edges of the bi-forwarding graph based on the network traffic routing paths. In this way, each traffic flow in the network is transformed into a bi-directional forwarding graph. Then TITAN feeds the above bidirectional forwarding graph into a Graph Convolutional Network (GCN) to detect whether the flow is a saturation attack flow. The experimental results show that TITAN can effectively detect saturation attacks in SDNs with a detection accuracy of more than 97%.

A Survey on the Latest Intrusion Detection Datasets for Software Defined Networking Environments

Software Defined Networking (SDN) threats make network components vulnerable to cyber-attacks, creating obstacles for new model development that necessitate innovative security countermeasures, like Intrusion Detection Systems (IDSs). The centralized SDN controller, which has global view and control over the whole network and the availability of processing and storing capabilities, makes the deployment of artificial intelligence-based IDS in controllers a hot topic in the research community to resolve security issues. In order to develop effective AI-based IDSs in an SDN environment, there must be a high-quality dataset for training the model to offer effective and accurate attack prediction. There are some intrusion detection datasets used by researchers, but those datasets are either outdated or incompatible with the SDN environment. In this survey, an overview of the published work was conducted using the InSDN dataset from 2020 to 2023. Also, research challenges and future work for further research on IDS issues when deployed in an SDN environment are discussed, particularly when employing machine learning and deep learning models. Moreover, possible solutions for each issue are provided to help the researchers carry out and develop new methods of secure SDN.

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Many research efforts have gone into upgrading antiquated communication network infrastructures with better ones to support contemporary services and applications. Smart networks can adapt to new technologies and traffic trends on their own. Software-defined networking (SDN) separates the control plane from the data plane and runs programs in one place, changing network management. New technologies like SDN and machine learning (ML) could improve network performance and QoS. This paper presents a comprehensive research study on integrating SDN with ML to improve network performance and quality-of-service (QoS). The study primarily investigates ML classification methods, highlighting their significance in the context of traffic classification (TC). Additionally, traditional methods are discussed to clarify the ML outperformance observed throughout our investigation, underscoring the superiority of ML algorithms in SDN TC. The study describes how labeled traffic data can be used to train ML models for appropriately classifying SDN TC flows. It examines the pros and downsides of dynamic and adaptive TC using ML algorithms. The research also examines how ML may improve SDN security. It explores using ML for anomaly detection, intrusion detection, and attack mitigation in SDN networks, stressing the proactive threat-detection and response benefits. Finally, we discuss the SDN-ML QoS integration problems and research gaps. Furthermore, scalability and performance issues in large-scale SDN implementations are identified as potential issues and areas for additional research.

Securing SDN: Hybrid autoencoder-random forest for intrusion detection and attack mitigation

Software Defined Networking (SDN) has revolutionized network administration by providing centralized management through software, enabling traffic adjustment independent of the data plane. Despite the benefits, SDN networks are prone to security threats from external sources, thus necessitating the implementation of security measures. Unfortunately, most existing efforts have been just a simple mapping of earlier solutions into the SDN environments. This paper addresses the problem of SDN security based on deep learning in a purely native SDN environment, where a Deep Learning intrusion detection module is tailored to a native SDN environment. In particular, we propose a hybrid Deep AutoEncoder with a Random Forest classifier model (DAERF) to enhance intrusion detection performance in a native SDN environment. The proposed model is incorporated into a novel adaptive framework for attack mitigation in SDN environments. The proposed framework consists of a three-layer protection mechanism for detecting and preventing attacks. It is based on entropy-based detection, hybrid machine learning in the control layer and proactive services monitoring in the application layer. Experimental results have shown that our DEARF proposed autoencoder model achieved anomaly detection rates in excess of 98% in stand-alone mode as well as when incorporated within the framework, making it highly solution for next generation SDN networks.

Enhanced Anomaly Detection Framework for 6G Software-Defined Networks: Integration of Machine Learning, Deep Neural Networks, and Dynamic Telemetry

The advent of 6G networks ushers in a new era of intelligent network management, necessitating robust security measures to safeguard against emerging threats. This paper presents a comprehensive framework for anomaly detection tailored specifically for 6G Software-Defined Networks (SDNs), leveraging innovative ML), (DL), and dynamic telemetry techniques. The proposed framework, termed Anomaly Detection System for 6G SDNs, integrates ensemble learning (EL) algorithms and deep neural networks (DNNs) to detect anomalies within network traffic. Beginning with the preprocessing and feature selection stages, the proposed system employs an amalgam EL method to enhance the efficacy of anomaly detection. Datasets including CICDDOS2019, NSL KDD, CIC_IDS2017, and NB2015 undergo dimensionality reduction and feature subset determination to optimize performance. Furthermore, dynamic telemetry is seamlessly integrated into the proposed, enabling real- time monitoring and adaptive response mechanisms within SDN environments. By harnessing the flexibility and programmability of SDNs, the framework ensures a proactive defense against evolving threats, bolstering the security posture of 6G networks. Experimental evaluations demonstrate the effectiveness of ADS6SDN across diverse datasets, achieving high accuracies while minimizing false alarm rates. In conclusion, integrating ML, DL, and dynamic telemetry within the proposed approach offers a potent solution for enhancing the security and responsiveness of 6G SDNs. By leveraging the inherent advantages of SDN architectures, the framework not only fortifies network defenses against emerging threats but also ensures adaptability to the budding scenario of next-generation telecommunications.

A Comprehensive Survey of Distributed Denial of Service Detection and Mitigation Technologies in Software-Defined Network

The widespread adoption of software-defined networking (SDN) technology has brought revolutionary changes to network control and management. Compared to traditional networks, SDN enhances security by separating the control plane from the data plane and replacing the traditional network architecture with a more flexible one. However, due to its inherent architectural flaws, SDN still faces new security threats. This paper expounds on the architecture and security of SDN, analyzes the vulnerabilities of SDN architecture, and introduces common distributed denial of service (DDoS) attacks within the SDN architecture. This article also provides a review of the relevant literature on DDoS attack detection and mitigation in the current SDN environment based on the technologies used, including statistical analysis, machine learning, policy-based, and moving target defense techniques. The advantages and disadvantages of these technologies, in terms of deployment difficulty, accuracy, and other factors, are analyzed. Finally, this study summarizes the SDN experimental environment and DDoS attack traffic generators and datasets of the reviewed literature and the limitations of current defense methods and suggests potential future research directions.

Evaluating the Isolation Forest Method for Anomaly Detection in Software-Defined Networking Security

The research addresses the critical anomaly detection problem in Software-Defined Networking (SDN), a domain where network integrity and security are paramount. Employing the Isolation Forest algorithm, a machine learning model renowned for its efficacy in identifying outliers, the study systematically generates synthetic network traffic data to train and test the model's detection capabilities. The methodology encompasses simulating a range of contamination rates to reflect varying degrees of anomalous activities within the network. Key findings indicate that while the model exhibits potential in anomaly detection, as reflected by the progressive increase in triggered alerts and policy changes, its performance metrics, such as precision, recall, F1-score, and AUC, reveal limitations in its current application. The research contributes to the field by providing a detailed analysis of the Isolation Forest algorithm's performance in an SDN context and laying the groundwork for future enhancements in machine learning-based security measures within these networks.

Transcritical and Hopf Bifurcation Analysis of Cyberattacks in Software-Defined Networking

This research work examines the dynamics of cyberattacks using the lenses of transcritical and Hopf bifurcations in Software-Defined Networking (SDN) systems. The motivation of this research is to determine the bifurcation points that are crucial to evaluate the different movements of the cyberattacks in the network system. The bifurcation points can then be used to create an effective mitigation plans for better security of SDN. We examine the circumstances surrounding these bifurcations and how they affect the resilience and stability of SDN systems. The efficiency of these tactics is illustrated by numerical modeling in Anaconda, which adds to the understanding of the dynamics of cyberattacks in SDN systems, especially with regard to transcritical and Hopf bifurcations.

Detection of Distributed Denial of Service Attacks in Software Defined Networks by Using Machine Learning

Within the sphere of Software-Defined Networking (SDN) — an innovative architectural paradigm that segregates the control plane from the data plane — a paramount concern is the defense against Distributed Denial of Service (DDoS) assaults. These attacks pose a significant threat to the integrity and operational sustainability of SDN infrastructures, potentially leading to extensive system disruptions and financial losses.To address this challenge, our study introduces an innovative approach utilizing machine learning strategies to enhance the detection of DDoS threats. We employed a trio of classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), applied to a publicly available SDN dataset specific to DDoS attacks. Our methodology integrates a blend of feature selection techniques, including Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and t-Distributed Stochastic Neighbor Embedding (t-SNE), with the aim of refining the accuracy of our classifications.In a comparative analysis with existing models, our innovative application of KNN in conjunction with RFE demonstrated exceptional performance, achieving an accuracy of 99.97%, a precision of 99.98%, a recall of 99.96%, and an F1-score of 99.97%. This breakthrough indicates a significant advancement in the field of SDN security.