Enhancing Security in Software-Defined Networks Using Artificial Intelligence Techniques

Software-defined networking (SDN) is an emerging network architecture that decouples the control plane from the data plane, enabling centralized programmability and management of networks. While SDN offers benefits like flexibility, scalability, and automation, it also introduces new security vulnerabilities. This literature review analyzes the current state of research on SDN security in three key domains – Internet of Things (IoT) environments, cloud computing, and traditional enterprise networks. A systematic review methodology was followed to search, select, and review 53 relevant studies published in the past 5 years. The analysis focuses on identifying common SDN threat vectors, security solutions proposed leveraging SDN programmability and evaluating their effectiveness based on results from simulations, testbed experiments, and initial real-world implementations. Key findings of the review include lack of authentication, susceptibility to DDoS attacks, and flow rule conflicts as major security issues in SDN across domains. Dynamic traffic monitoring, access control, policy orchestration, and virtualized security functions are commonly proposed techniques to enhance SDN security. However, limitations exist in robustness testing at scale, emerging paradigms like fog computing, and quantitatively comparing SDN security with legacy networks. As SDN adoption expands, focused efforts are needed to address these research gaps through innovations in data-driven security, coordinated security policy, and emphasizing SDN controller security. This review provides valuable insights into the current state of SDN security research and informs future efforts needed in this important area

Software Defined Networking (SDN) is disruptive networking technology which adopts a centralised framework to facilitate fine-grained network management. However security in SDN is still in its infancy and there is need for significant work to deal with different attacks in SDN. In this paper we discuss some of the possible attacks on SDN switches and propose techniques for detecting the attacks on switches. We have developed a Switch Security Application (SSA)for SDN Controller which makes use of trusted computing technology and some additional components for detecting attacks on the switches. In particular TPM attestation is used to ensure that switches are in trusted state during boot time before configuring the flow rules on the switches. The additional components are used for storing and validating messages related to the flow rule configuration of the switches. The stored information is used for generating a trusted report on the expected flow rules in the switches and using this information for validating the flow rules that are actually enforced in the switches. If there is any variation to flow rules that are enforced in the switches compared to the expected flow rules by the SSA, then, the switch is considered to be under attack and an alert is raised to the SDN Administrator. The administrator can isolate the switch from network or make use of trusted report for restoring the flow rules in the switches. We will also present a prototype implementation of our technique.

The east-west interface in Software Defined Networking (SDN) plays a crucial role in enabling inter-controller communication, which is vital for scalability, load balancing, and fault tolerance in distributed SDN environments. Despite its importance, this interface remains vulnerable to serious security threats, such as Man-in-the-Middle (MitM), unauthorized access, False Data Injection (FDI), and Distributed Denial-of-Service (DDoS) attacks. Unlike previous studies and reviews that focus broadly on SDN security, this paper presents a comprehensive review of the current security challenges and proposed solutions that are specific to the east-west interface. The literature is organized into five categories: cryptographic techniques, authentication and access control, blockchain-based mechanisms, Machine Learning (ML) approaches, and hybrid models. In addition, an analytical contribution is provided by introducing a threat-solution coverage matrix that maps each reviewed solution to the specific types of attacks it mitigates. This analysis highlights under-addressed vulnerabilities and uncovers critical research gaps. Future research directions are provided, including the adoption of zero-trust architectures and the need for standardized benchmarking protocols.

Software Defined Network (SDN) is the new era of networking technology based on a centralized controller that separates the switch hardware from its operating software. The most important challenge is the security of SDN and the most prominent attack is the Distributed Denial of Service (DDoS) attack. Some of the research work done so far detects DDoS attacks using a threshold, which is usually assumed without proper scientific reason and hence may not be always accurate. The mitigation techniques used by some researchers block the host from sending the network traffic beyond a threshold, by installing drop rules in the flow table of the switch connected to that host. Doing so will not only block the attack traffic but also the genuine ones from other applications of that host. In this paper, we propose a model that calculates the threshold limit for the type of applications sending data to a particular switch, in real-time using a machine learning (ML) model, and determines whether that application traffic is DDoS traffic. After the detection, only application type sending DDoS traffic is blocked while other genuine applications are allowed to send the network traffic without any interruption. The use of a dynamic threshold, based on the current network traffic, will help in detecting DDoS efficiently.

An Approach for Supervising the Security Threats using Software Defined Networks

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.

An AI driven double-tier detection system, means this is a security framework that works in two layers to increase accuracy and reliability of threat identification, this is presented in this research. The system designs to tackle rising Software Defined Network (SDN) security threats especially in healthcare environment to the risk of malware, and ransomware attacks, is powered by Deep Learning (DL) and Machine Learning (ML) technologies to work together, as a static and dynamic scanning methods. Using this, execution traces and the Application Programming Interface (API) call sequences are integrated with Generative Adversarial Networks (GANs) and autoencoder based predictive analysis to improve the threat detection. This is a continuous training system that ensures medical data’s protection while maintaining compliance with health regulations and creates a secure operational environment. The double tier eXtreme Gradient Boosting (XGBoost) performance reaches 99.60% accuracy with an F1 score of 0.9966 and outperforming the benchmark model at 98.80% accuracy and F1 score 0.988. Further, a two-layer Light Gradient Boosting Machine (LightGBM) model achieves 99.32% accuracy with an F1 score of 0.9934, in contrast to which at 98.96% accuracy and an F1 score of 0.9895, beats its benchmark counterpart. Furthermore, the Deep Neural Network (DNN) system with bi layered also achieves 99.13% accuracy and F1 score of 0.9915. This study presents novel countermeasures for these SDN and healthcare security threats that warrant a breakthrough in the field of cybersecurity. The presented security model is agile and strong which offer real time network assessment and continue to detect emerging threats thus improving the protection of critical healthcare infrastructure.

Software Defined Networking (SDN) is an emerging architecture which decouples networks control plane and data plane physically. It makes control plane programmable trough a centralized controller, and builds intelligent and flexible networks. The OpenFlow is one of the most famous SDN protocols, which acts as a southbound interface between control plane and data plane. In this survey, SDN implementation approaches and different southbound interfaces, beside different version of OpenFlow, are introduced. In addition to general architecture of SDN, different wireless architectures are discussed. Here, also potential SDN’s applications and research areas including hot topics such as Information Centric Networks, Cloud and datacenters, multimedia, wireless and mobile networks over SDN are reviewed.

Software Defined Networking (SDN) is the new promise towards an easily configured and remotely controlled network. Based on Centralized control, SDN technology has proved its positive impact on the world of network communications from different aspects. Security in SDN, as in traditional networks, is an essential feature that every communication system should possess. In this paper, we propose an SDN security design approach, which strikes a good balance between network performance and security features. We show how such an approach can be used to prevent DDoS attacks targeting either the controller or the different hosts in the network, and how to trace back the source of the attack. The solution lies in introducing a third plane, the security plane, in addition to the data plane, which is responsible for forwarding data packets between SDN switches, and parallel to the control plane, which is responsible for rule and data exchange between the switches and the SDN controller. The security plane is designed to exchange security-related data between a third party agent on the switch and a third party software module alongside the controller. Our evaluation shows the capability of the proposed system to enforce different levels of real-time user-defined security with low overhead and minimal configuration.

In the last couple of years Software Defined Network (SDN) have come into existence which empowers network operators with more flexibility to manage and program their network. This type of network solves the limitation of legacy networks. Data plane and control planes are separated from each other as a result data plane devices simple act as a packet forwarding device and leaving the decision making part to a centralized system called controller. Though it has a lot of advantages, still security of SDN is an open issue. In modern day living wireless sensor network (WSN) technologies come across all most all areas which creates another research dimension called IoT where sensors and actuators blend in one piece. Application of SDN architecture in the IoT environment is a higher challenge. In this article we will present the security challenges in SDN and a secured architecture for IoT in an SDN based network.

Data-driven software defined network attack detection : State-of-the-art and perspectives

Cyber attacks are a major concern for network administrators as the occurrences of such events are continuously increasing on the Internet. Software-defined networks (SDN) enable many management applications, but they may also become targets for attackers. Due to the separation of the data plane and the control plane, the controller appears as a new element in SDN networks, allowing centralized control of the network, becoming a strategic target in carrying out an attack. According to reports generated by security labs, the frequency of the distributed denial of service (DDoS) attacks has seen an increase in recent years, characterizing a major threat to the SDN. However, few research papers address the prevention of DDoS attacks on SDN. Therefore, this work presents a Systematic Mapping of Literature, aiming at identifying, classifying, and thus disseminating current research studies that propose techniques and methods for preventing DDoS attacks in SDN. When answering these questions, it was determined that the SDN controller was vulnerable to possible DDoS attacks. No prevention methods were found in the literature for the first phase of the attack (when attackers try to deceive users and infect the host). Therefore, the security of software-defined networks still needs improvement over DDoS attacks, despite the evident risk of an attack targeting the SDN controller.

The rapid evolution of next-generation networks (NGNs), driven by 5G, IoT, edge computing, and software-defined networking, has introduced new opportunities alongside complex security challenges. Traditional intrusion detection and prevention systems (IDS), built on signature-based and anomaly-based methods, struggle to cope with the scale, heterogeneity, and dynamic threat landscape of NGNs. In response, artificial intelligence (AI) has emerged as a powerful enabler of modern IDPS. This review surveys AI-powered approaches, beginning with classical machine learning methods such as decision trees, support vector machines, and random forests, and then examining deep learning architectures including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and autoencoders. It further analyses hybrid frameworks that integrate ensemble learning, federated learning, and meta-learning, as well as specialised methods tailored for SDN, IoT, edge, and cloud/5G environments. Benchmark datasets, including NSL-KDD, CICIDS2017, UNSW-NB15, Bot-IoT, IoT-23, and TON_IoT, are reviewed, highlighting their contributions and limitations. The paper identifies key challenges, including dataset scarcity, generalisation gaps, computational overhead, adversarial robustness, explainability, and privacy. Future directions emphasise the need for realistic NGN datasets, lightweight yet accurate architectures, privacy-preserving and federated frameworks, and integrated detection and prevention mechanisms. Overall, AI-powered IDPS demonstrate significant potential to secure NGNs, but realising this vision will require advances that balance accuracy, efficiency, interpretability, and resilience.

Learning ability is the basic characteristic of human intelligence. The July 1, 2005 issue of Science published a list of 125 important questions in science. Among them, the question 94 “What are the limits of learning by machines?”. The annotation “Computers can already beat the world’s best chess players, and they have a wealth of information on the Web to draw on. But abstract reasoning is still beyond any machine”. In recent artificial intelligence has made great progresses. In 1997, the rise of the man-machine war, IBM Supercomputer Deep Blue defeated the chess master Garry Kasparov. On February 14, 2011, IBM’s Watson supercomputer won a practice round against Jeopardy champions Ken Jennings and Brad Rutter. In March 2016, Google DeepMind’s AlphaGo sealed a 4-1 victory over a South Korean Go grandmaster Lee Se-dol. This paper focuses on the machine learning methods of AlphaGo, including reinforcement learning, deep learning, deep reinforcement learning, analysis of the existing problems and the latest research progress. Deep reinforcement learning is the combination of deep learning and reinforcement learning, which can realize the learning algorithm from the perception to action. Simply said, this is the same as human behavior, input sensing information such as vision, and then, direct output action through the deep neural network. Deep reinforcement learning has the potential to learn a variety of skills for the robot to achieve full autonomy. Even though reinforcement learning is practiced successfully, but feature states need to manually set, for complex scene is a difficult thing, especially easy to cause the dimension disaster, and expression is not good. In 2010, Sascha Lange and Martin Riedmiller proposed deep auto-encoder neural networks in reinforcement learning to extract feature, which is used to control the visual correlation. In 2013, DeepMind proposed deep Q-network (DQN) in NIPS 2013, using convolution neural network to extract features, and then applied in reinforcement learning. They continue to improve and published an improved version of DQN on Nature in 2015, which has aroused widespread concern. In order to break through the limits of learning by machines, cognitive machine learning is proposed, which is the combination of machine learning and brain cognition, so that the machine intelligence is constantly evolving, and gradually reaches the human level of artificial intelligence. A cognitive model entitled Consciousness And Memory (CAM) is proposed by author, which consists of memory, consciousness, high-level cognitive functions, perception and motor. High-level cognitive functions of the brain include learning, language, thinking, decision making, emotion, and so on. Learning is a course to accept the stimulus through the nervous system and obtain new behavior, habits and accumulation experience. According to the current research progress of brain science and cognitive science, cognitive machine learning may be interested in learning emergence, procedural memory knowledge learning, learning evolution and so on. For intelligence, so-called evolution is refers to the learning of learning and the structure also follows the change. It is important to record the learning result by structure changing and improve the learning method.

In recent years, Software Defined Networking (SDN) has enabled total control over the network's data flow. SDN acts as a centralised point of administration for data and traffic management. Due to the fact that SDN is an open source software, it is more vulnerable to security concerns. Security policies must also be adhered to, since this would expose the controller to the greatest attacks. DDOS and DOS assaults are more prevalent in SDN controllers. DDOS is a damaging assault that disrupts the usual flow of communication and initiates an overflow of flooded packets, thereby shutting down the system. Machine Learning approaches assist in identifying the network's hidden and unexpected patterns, hence aiding in the analysis of the network's flow. All classified and unclassified approaches can assist in detecting hostile flows depending on specific factors such as packet flow, time length, precision, and accuracy rate. To identify DDOS assaults, researchers employed Bayesian Networks, Wavelets, Support Vector Machines, and KNN. According to the review, KNN offers superior results due to its increased accuracy and reduced false positive rate for detection. We explore the various strategies used in DDoS detection and examine new improvements in intrusion detection in software defined networks in this article.