An Analysis of Dynamic DDoS Entry Point Localization in Software-Defined WANs

Distributed Denial of Service (DDoS) attack is a serious threat to commercial service network. DDoS attack has been studied for years. However, detecting and relieving DDoS attacks are still a problem. Especially, nowadays more and more DDoS attacks produce heavy network traffic, it is hard to response rapidly because that needs high processing performance to process massive traffic data. With big data technology, volumes of network traffic data can be processed much faster. Apache Spark can process a great amount of data in a reasonable time so that DDoS attack can be detected in time. Besides, it is difficult to modify the network configuration in traditional network. With Software-Defined Networking (SDN), a new paradigm in networking, networking can be controlled by programs, which makes modifying the network configuration easier. In this paper, a DDoS detection and mitigation system framework in SDN is introduced, a framework that can control network based on analyzing the network traffic data. Comparing to the traditional defense methods of DDoS attack, the framework can response to DDoS attack by rules automatically.

Software Defined Networking (SDN) is widely used in many practical contexts and provides a method for managing networks by separating the data plane from the control plane. However, because of its centralized control logic, SDN is particularly susceptible to Distributed Denial of Service (DDoS) attacks. In numerous studies, methods based on Machine Learning (ML) have been proposed to identify DDoS attacks in the context of SDN. However, the proposed methods are resource intensive and unreliable in a large-scale SDN where a huge amount of traffic data is produced from control and data planes. This may exhaust computational resources, deteriorate the performance of the network, or even result in the shutdown of network systems as a result of resource exhaustion. To deal with the above issues, this paper presents a real-time DDoS detection and mitigation method in a multi-controller SDN environment using cloud computing. We propose to run the Machine Learning development cycle on a secured server in a virtual machine. Then we deploy back the trained model into the SDN controllers. All the exchanges between the virtual machine and the controllers are secured. In this way, we configure these controllers to detect and mitigate DDoS in SDN in real-time. Through useful experiments, we demonstrate that our method is secure, reliable, and efficient.

Software-defined networking (SDN) is a new networking architecture having the concept of separation of control plane and data plane that leads the existing networks to be programmable, dynamically configurable and extremely flexible. This paradigm has huge benefits to organizations and large networks, however, its security is major issue and Distributed Denial of Service (DDoS) Attack has become a serious concern for the working of SDN. In this article, we have proposed a taxonomy of DDoS Defense Mechanisms in SDN Environment. We have categorized the various DDoS detection and mitigation techniques with respect to switch intelligence, Defense Deployment, Defense Activity and Network Flow Activities.

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.

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.

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.

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.

Software Defined Network (SDN) is a new network construction. But due to its construction, SDN is vulnerable to be attacked by Distributed Denial of Service (DDoS) attack. So it is important to detect DDoS attack in SDN network. This paper presents a DDoS detection scheme based on k-means algorithm in SDN environment. The establishment of this scheme is based on the two hypotheses that the daily network works normally most of the time, and there is a significant difference between the data characteristics of normal situation and abnormal situation. At the same time, these two hypotheses are also true to the daily network condition. After demonstrating the validity of k-means clustering algorithm, the paper proposes 5 flow table features that can be used to detect DDoS attacks. Finally, the DDoS detection scheme was tested by simulation experiment. The test results showed that the method proposed by the author could effectively detect DDoS, with an average success rate of 97.78%.

The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by using blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems.

With the emergence of smart devices and the declining market value of sensing systems, the implementation of the internet of things (IoT) is gaining momentum. IoT has become an integral part of many smart systems such as smart buildings, smart cities, and smart heath, etc. Despite many advantages, from its inception, IoT system is vulnerable to many threats. Such systems are more likely to be attacked and breached, by denial of service (DoS) or distributed denial of service (DDoS) attacks. DDoS attacks are one of the Internet security's most disruptive threats. The towering volume of smart devices with a greater level of heterogeneity increases the severity of this attack. To date, there is no protocol that guarantees complete protection of IoT systems. To enable the resiliency, continuous monitoring, along with an adaptive decision-making system, is essential. These challenges can be addressed with the help of software-defined networking (SDN), which can effectively handle the IoT devices. Moreover, the centralized provisioning of SDN enables a secured and adaptive IoT environment that can effectively handle the DDoS attack. The software-defined internet of things (SDIoT) framework based on SDx paradigm consists of a controller pool, SD-IoT switches integrated with 190an IoT gateway, and IoT devices. In this chapter, a broad range of various DDoS attacks and detection methods for the IoT environment used in the SDN paradigm have been analyzed. Finally, different future scopes of this evolving field have been discussed.

The exponential growth of interconnected systems across the Internet of Things (IoT), Software-Defined Networks (SDN), and cloud environments has led to a drastic increase in the scale and advancement of Distributed Denial of Service (DDoS) attacks. Conventional defences based on machine learning are often static, traffic-centric, and lack adaptivity to dynamic network behaviour, resulting in high false-positive rates and delay in mitigation responses. To address these challenges, this paper presents a conceptual framework for an AI-driven, context-aware DDoS detection and mitigation system. The proposed approach employs a optimized Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) model for efficient, real-time detection of DDoS attacks. The model captures spatial and temporal correlations in traffic while incorporating contextual parameters such as Quality of Service (QoS), Service Level Agreement (SLA) priority, and workload telemetry. These contextual attributes enable the model to distinguish legitimate network fluctuations from malicious activities, so that the false positive rate will be reduced without compromising accuracy. For adaptive response, a Reinforcement Learning (RL)-based mitigation layer—exploiting Deep Q-Network (DQN) and Actor–Critic (AC) variants—continuously learns optimal mitigation actions based on real-time feedback. The interaction between detection and mitigation components forms a context-aware feedback loop, enabling dynamic policy refinement and continuous system improvement. Comprehensive literature analysis reveals that existing AI-based DDoS defense mechanisms lack integration between detection and mitigation, and often overlook contextual intelligence. The proposed framework bridges this gap by introducing a self-adaptive, feedback-driven defense system capable of maintaining detection precision, low latency, and QoS preservation in heterogeneous environments.

Exploring current solutions against DDoS attacks in SDN environment

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.

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.

Distributed Denial of Service (DDoS) attacks have emerged as one of the most pervasive and damaging threats to network security, disrupting services and incurring substantial financial costs. Machine learning (ML) has been widely explored as a potential solution to enhance DDoS detection and mitigation. This systematic review evaluates the effectiveness of ML techniques in detecting DDoS attacks, synthesizing findings from studies published between 2004 and 2024. The review analyzes models such as Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (K-NN) based on key performance metrics like accuracy, precision, recall, and F1-score. A comprehensive search of multiple databases, including Web of Science, IEEE, Scopus, ScienceDirect, and Google Scholar, resulted in 19 studies that met inclusion criteria. The findings show that ensemble methods, particularly Random Forest, consistently outperformed other models in terms of detection rates, mainly due to their ability to handle large feature sets and reduce overfitting. Support Vector Machines also performed well in specific scenarios. However, their effectiveness was sometimes limited by computational complexity and the dataset size. K-Nearest Neighbors showed mixed results, depending on the nature of the attack patterns. This review emphasizes the potential of ensemble learning approaches for DDoS detection, demonstrating their robustness in dynamic environments. The review identifies key gaps in the existing research, including the need for better feature selection and exploring deep learning techniques to enhance DDoS detection accuracy and adaptability further. This study contributes valuable insights into the strengths and limitations of ML-based DDoS detection models, offering a foundation for future advancements in the field. It underscores the importance of continued research into hybrid and deep learning models to address the evolving and increasingly sophisticated nature of DDoS attacks in real-world applications.