DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking

This study presents the development of a realistic and comprehensive SDN-ATK dataset designed to evaluate the effectiveness of machine learning (ML) and deep learning (DL) approaches for attack detection in Software-Defined Networking (SDN) environments. Unlike existing datasets, SDN-ATK explicitly includes attacks targeting key SDN components such as SDN controllers and OpenFlow switches, addressing a critical gap in current research. We evaluated three ML (XGBoost, Random Forest, and Decision Tree) and three DL (Convolutional Neural Network (CNN), Feed-forward Neural Network (FNN), and Long Short-Term Memory (LSTM)) algorithms across binary and multiclass classification tasks to assess detection performance. Our results demonstrate that DL models, particularly FNN and CNN outperform ML counterparts, achieving 98–99% accuracy, precision, and recall in binary classification. Explainability analyses were conducted using SHAP (SHapley Additive explanations) on the XGBoost model, offering valuable insights into the importance of feature and improving transparency in ML-based attack detection. The study’s findings provide critical guidance for both academia and industry, highlighting that within our Ryu-based SDN testbed, DL models demonstrated more reliable and balanced performance for large-scale attack detection. This work lays a solid foundation for future research, including developing real-time, intelligent, and explainable intrusion detection systems for SDN environments.

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.

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.

Software-Defined Networking (SDN) offers centralized control and programmability, transforming network management but also introducing vulnerabilities, particularly to Distributed Denial of Service (DDoS) attacks that can overwhelm the control plane and disrupt network functionality. Traditional DDoS detection methods, including rule-based systems and conventional machine learning models, often fall short in SDN due to high false-positive rates and limited adaptability to evolving network traffic. While recent deep learning approaches show promise, they continue to face challenges with real-time adaptability and scalability in SDN environments. In this study, we propose Attention-Enhanced Cross-Entropy (AECE), a novel Deep Neural Network (DNN)-based DDoS detection model that integrates attention mechanisms to prioritize critical features in network traffic data, allowing the model to focus on patterns indicative of DDoS attacks. A core innovation in AECE is the F-Balanced Cross-Entropy (FBCE) Loss function, which combines cross-entropy with an F1-score-based component to balance precision and recall, effectively reducing both false positives and false negatives. Additionally, AECE incorporates ReLU and GELU activations, batch normalization, dropout, and the Adamax optimizer to enhance learning stability and computational efficiency. Experimental results demonstrate that the proposed system achieves high detection accuracy, significantly outperforming existing DDoS detection methods and providing a robust, low-latency solution to safeguard SDN infrastructures against evolving DDoS threats.

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The evolution from conventional network frameworks to Software-Defined Networks (SDNs) marks a pivotal shift in network management. SDNs offer a unique architecture where a centralized controller orchestrates network traffic, separating the control plane from the data plane. This architecture not only brings enhanced flexibility, agility, and scalability to network operations but also introduces specific security challenges, primarily due to the centralization of control, which can be a potential Single Point of Failures (SPOFs). Addressing these security concerns, this paper introduces an innovative machine learning-based Intrusion Detection System (IDS) tailored for SDNs, by proposing a three-stage CatBoost Classifier (3CC). This classifier is specifically designed for efficient and effective intrusion and vulnerability detection within SDN environments. The multistage aspect of the classifier allows for refined and detailed analysis, catering to a diverse array of attack vectors. Another key feature of our approach is the utilization of real-world data from SDN environments, which ensures that our model is tested and validated under realistic and current conditions. Furthermore, the integration of GPU acceleration significantly enhances the training speed of our model, a critical factor given the voluminous nature of network data. The proposed model, 3CC, demonstrates remarkable performance in our evaluations, achieving an accuracy of 99.8966%. This high level of precision in detecting and identifying various network intrusions and vulnerabilities underlines the efficacy of our approach. Our paper contributes significantly to the field of SDN security, offering a sophisticated, multistage classification solution that leverages the latest in machine learning techniques and real-world applicability.

Software defined networking (SDN) increasingly integrates multiple controllers from diverse vendors to enhance network scalability, flexibility, and reliability. However, such heterogeneous deployments pose significant security threats, especially at the east-west interface which is connecting these controllers. Existing solutions are inadequate for ensuring robust protection across multi-vendor SDN environments as most of them are meant to a specific type of attacks, use centralized solution, or designed for homogeneous SDN environments. This study proposes a blockchain-based security framework to address existing security gaps within heterogeneous SDN environments. The framework establishes a decentralized, robust, and interoperable security layer for distributed SDN controllers. By utilizing the Ethereum blockchain with customized smart contract-based checks, the proposed approach enables mutual authentication among controllers, secures data exchange, and controls network access. The framework effectively mitigates common SDN threats such as distributed denial-of-service (DDoS), man-in-the-middle (MitM), false data injection, and unauthorized access. Experimental results highlight the practicality of the solution, achieving a stable throughput of approximately 20 transactions per second with an average authentication latency of 28-40 ms. These results demonstrate that the proposed framework not only enhances inter-controller communication security but also maintains the network performance, making it a reliable and scalable solution for real-world SDN deployments.

Achieving energy efficiency (EE) fairness among heterogeneous mobile devices will become a crucial issue in future wireless networks. This paper investigates a deep learning (DL) approach for improving EE fairness performance in interference channels (IFCs) where multiple transmitters simultaneously convey data to their corresponding receivers. To improve the EE fairness, we aim to maximize the minimum EE among multiple transmitter–receiver pairs by optimizing the transmit power levels. Due to fractional and max-min formulation, the problem is shown to be non-convex, and, thus, it is difficult to identify the optimal power control policy. Although the EE fairness maximization problem has been recently addressed by the successive convex approximation framework, it requires intensive computations for iterative optimizations and suffers from the sub-optimality incurred by the non-convexity. To tackle these issues, we propose a deep neural network (DNN) where the procedure of optimal solution calculation, which is unknown in general, is accurately approximated by well-designed DNNs. The target of the DNN is to yield an efficient power control solution for the EE fairness maximization problem by accepting the channel state information as an input feature. An unsupervised training algorithm is presented where the DNN learns an effective mapping from the channel to the EE maximizing power control strategy by itself. Numerical results demonstrate that the proposed DNN-based power control method performs better than a conventional optimization approach with much-reduced execution time. This work opens a new possibility of using DL as an alternative optimization tool for the EE maximizing design of the next-generation wireless networks.

Software-defined Networking (SDN) is the key catalyst in the next-generation network. Cloud computing has shown rapid growth due to the development of SDN. Due to the growth in security difficulties and threats, SDN networks will have new security issues to overcome, and a new layer of security procedures, such as intrusion detection systems (IDS), is required. Nowadays, web services are flexible, economical, and user-friendly. Although this may be true, they still seem to be more susceptible to security issues than legacy systems. Therefore, SDN architecture also incorporates several machine learning methods to find classify malicious attacks and legitimate traffic. We include a rigorous study of the current strategies for malicious traffic identification and we define the classical machine learning method's shortcomings, and a deep learning technique is also being investigated to reach higher levels of efficiency and accuracy. This article explores SDN security and IDS about security concerns. A dataset of IDS, publicly accessible, KDD-CUP99, and NSL-KDD Dataset are used to determine the possible behaviour of security flaws.

DNS Security Extensions i.e. DNSSEC protects the backbone of Internet (i.e. Domain Name Service or DNS) against various types of attacks like cache poisoning attacks. Although it is the only security mechanism atop DNS, its adoption has been very limited. This is due to the various shortcomings of DNSSEC, one of them being increased payload size. In this paper, we propose a solution to reduce the DNSSEC packet size by leveraging the features of Software Defined Networking (SDN). The solution involves memorizing the ciphers of the Authoritative Name Servers in SDN controller so as to reduce the size of subsequent queries to the same name server.

With the increasing rate and types of cyber attacks against information systems and communication infrastructures, many tools are needed to detect and mitigate against such attacks, for example, Intrusion Detection Systems (IDSs). Unfortunately, traditional Signature‐based IDSs (SIDSs) perform poorly against previously unseen adversarial attacks. Anomaly‐based IDSs (AIDSs) use Machine Learning (ML) and Deep Learning (DL) approaches to overcome these limitations. However, AIDS performance can be poor when trained on imbalanced datasets. To address the challenge of AIDS performance caused by these unbalanced training datasets, generative adversarial models are proposed to obtain adversarial attacks from one side and analyse their quality from another. According to extensive usage and reliability criteria for generative adversarial models in different disciplines, Generative Adversarial Networks (GANs) , Bidirectional GAN (BiGAN) , and Wasserstein GAN (WGAN) are employed to serve AIDS. The authors have extensively assessed their abilities and robustness to deliver high‐quality attacks for AIDS. AIDSs are constructed, trained, and tuned based on these models to measure their impacts. The authors have employed two datasets: NSL‐KDD and CICIDS‐2017 for generalisation purposes, where ML and DL approaches are utilised to implement AIDSs. Their results show that the WGAN model outperformed GANs and BiGAN models in binary and multiclass classifications for both datasets.

Intrusion detection is a major challenge for security experts in the cyber world. Traditional IDS failed to detect complex and unknown cyber-attacks. Machine learning has become a vibrant technology for cybersecurity. There exists several machine learning algorithms to detect intrusion. Most classifiers are well suited to detect the attacks. However, improving accuracy and detecting unknown attacks in existing IDSs is a great challenge. Therefore, the detailed comparative study of various machine learning approaches such as artificial neural networks, support vector machine, decision tree, and hybrid classifiers used by researchers for intrusion detection are done. Deep learning is an emerging approach which suits well for large data. Deep learning techniques find optimal feature set and classify low-frequency attacks better than other techniques. This study also summarizes literatures in deep learning approaches such as deep auto-encoder, Boltzmann machine, recurrent neural networks, convolutional neural networks, and deep neural networks. Moreover, the datasets used in various literatures and the analysis of deep learning approaches based on the performance metrics are also done. Future directions to detect intrusion are also provided. This study in fact will be helpful to develop IDS based on artificial intelligence approaches such as machine learning and deep learning.KeywordsMachine learningIntrusion detectionFeature selectionDeep learningCyber securityClassifier

Automated image classification is an essential task of the computer vision field. The tagging of images into a set of predefined groups is referred to as image classification. The implementation of computer vision to automate image classification would be beneficial because manual image evaluation and identification can be time-consuming, particularly when there are many images of different classes. Deep learning approaches are proven to overperform existing machine learning techniques in many fields in recent years, and computer vision is one of the most notable examples. The very deep neural network (VDCNN) is a powerful deep learning model for image classification, and this paper examines it briefly using MNIST handwritten digit dataset. This dataset is used to prove the efficacy of very deep neural networks over other deep learning models. The proposed study aims to comprehend the very deep neural network architecture used to accomplish a handwritten digit recognition task. The feasibility of the proposed model is evaluated using mean accuracy, validation accuracy, and standard deviation. The study results of the very deep neural network model are compared to a convolutional neural network and convolutional neural network with batch normalization. According to the results of the comparison study, very deep neural networks achieve high accuracy of 99.1% for a handwritten dataset. The outcome of the proposed work is used to interpret how well a very deep neural network performs when compared to the other two models of deep neural networks. This proposed architecture may be used to automate the classification of handwritten digits dataset.

Software-defined networking (SDN) has reformed the traditional approach to managing and configuring networks by isolating the data plane from control plane. This isolation helps enable centralized control over network resources, enhanced programmability, and the ability to dynamically apply and enforce security and traffic policies. The shift in architecture offers numerous advantages such as increased flexibility, scalability, and improved network management but also introduces new and notable security challenges such as Distributed Denial-of-Service (DDoS) attacks. Such attacks focus on affecting the target with malicious traffic and even short-lived DDoS incidents can drastically impact the entire network’s stability, performance and availability. This comprehensive review paper provides a detailed investigation of SDN principles, the nature of DDoS threats in such environments and the strategies used to detect/mitigate these attacks. It provides novelty by offering an in-depth categorization of state-of-the-art detection techniques, utilizing machine learning, deep learning, and federated learning in domain-specific and general-purpose SDN scenarios. Each method is analyzed for its effectiveness. The paper further evaluates the strengths and weaknesses of these techniques, highlighting their applicability in different SDN contexts. In addition, the paper outlines the key performance metrics used in evaluating these detection mechanisms. Moreover, the novelty of the study is classifying the datasets commonly used for training and validating DDoS detection models into two major categories: legacy-compatible datasets that are adapted from traditional network environments, and SDN-contextual datasets that are specifically generated to reflect the characteristics of modern SDN systems. Finally, the paper suggests a few directions for future research. These include enhancing the robustness of detection models, integrating privacy-preserving techniques in collaborative learning, and developing more comprehensive and realistic SDN-specific datasets to improve the strength of SDN infrastructures against DDoS threats.

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.