Distributed Denial Of Service Attacks In Software Defined Network Research Articles

Improved K-means-based solution for detecting DDoS attacks in SDN

Software Defined Networking (SDN) is a change to the traditional network, which can better face the complex network scenarios and meet the future network development needs. However, SDN faces many network security problems in the process of development, among which distributed denial-of-service (DDoS) attacks on SDN cause the most serious problem of communication paralysis. For the problem of unstable detection accuracy based on K-means algorithm detection scheme, a detection scheme based on density selection and mutual information improvement K-means algorithm (MIK-means) is proposed, which can well improve the stability and accuracy of the detection effect. Through a large number of experimental results show that the detection scheme maintains an average accuracy of more than 96% under different attack types and rates, while the detection scheme also maintains 5 6 s from response to completion of mitigation, which well reduces the computational overhead.

Feature-based real-time distributed denial of service detection in SDN using machine learning and Spark

Recently, software defined networking (SDN) has been deployed extensively in diverse practical domains, providing a new direction in network management by separating the control plane from the data plane. Nevertheless, SDN is vulnerable to distributed denial of service (DDoS) attacks resulting from its centralized controller. Several studies have been suggested to address the DDoS attacks in SDN utilizing machine learning approaches. However, these approaches are resource-intensive and cause performance degradation since they cannot perform effectively in large-scale SDN networks that generate vast traffic statistics. To handle all these challenges, we build a DDoS attack detection model in SDN using Spark as a big data tool to overcome the limitations of conventional data processing methods. Four machine learning algorithms are employed. The decision tree (DT) is elected to be used for real-time deployment based on the performance results, which indicates that it has the best accuracy of 0.936. The model performance is compared with state-of-the-art and shows an overall better performance.

Feature-based real-time distributed denial of service detection in SDN using machine learning and Spark

Recently, software defined networking (SDN) has been deployed extensively in diverse practical domains, providing a new direction in network management by separating the control plane from the data plane. Nevertheless, SDN is vulnerable to distributed denial of service (DDoS) attacks resulting from its centralized controller. Several studies have been suggested to address the DDoS attacks in SDN utilizing machine learning approaches. However, these approaches are resource-intensive and cause performance degradation since they cannot perform effectively in large-scale SDN networks that generate vast traffic statistics. To handle all these challenges, we build a DDoS attack detection model in SDN using Spark as a big data tool to overcome the limitations of conventional data processing methods. Four machine learning algorithms are employed. The decision tree (DT) is elected to be used for real-time deployment based on the performance results, which indicates that it has the best accuracy of 0.936. The model performance is compared with state-of-the-art and shows an overall better performance.

A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking.

Software-defined networking (SDN) is a revolutionary innovation in network technology with many desirable features, including flexibility and manageability. Despite those advantages, SDN is vulnerable to distributed denial of service (DDoS), which constitutes a significant threat due to its impact on the SDN network. Despite many security approaches to detect DDoS attacks, it remains an open research challenge. Therefore, this study presents a systematic literature review (SLR) to systematically investigate and critically analyze the existing DDoS attack approaches based on machine learning (ML), deep learning (DL), or hybrid approaches published between 2014 and 2022. We followed a predefined SLR protocol in two stages on eight online databases to comprehensively cover relevant studies. The two stages involve automatic and manual searching, resulting in 70 studies being identified as definitive primary studies. The trend indicates that the number of studies on SDN DDoS attacks has increased dramatically in the last few years. The analysis showed that the existing detection approaches primarily utilize ensemble, hybrid, and single ML-DL. Private synthetic datasets, followed by unrealistic datasets, are the most frequently used to evaluate those approaches. In addition, the review argues that the limited literature studies demand additional focus on resolving the remaining challenges and open issues stated in this SLR.

Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an SVM-based DDoS detection model shows superior performance. This comparative analysis offers a valuable insight into the development of efficient and accurate techniques for detecting DDoS attacks in SDN environments with less complexity and time.

Detecting and Mitigating DDoS Attacks in SDN Using Spatial-Temporal Graph Convolutional Network

With the development of data plane programmable Software-Defined Networking (SDN), Distributed Denial of Service (DDoS) attacks on the data plane increasingly become fatal. Currently, traditional attack detection methods are mainly used to detect whether a DDoS attack occurs and it is difficult to find the path that the attack flow traverses the network, which makes it difficult to accurately mitigate DDoS attacks. In this article, we propose a detection method based on Spatial-Temporal Graph Convolutional Network (ST-GCN) over the data plane programmable SDN, which maps the network into a graph. It senses the state of switches through In-band Network Telemetry (INT) with sampling, inputs the network state into the spatial-temporal graph convolutional network detection model, and finally finds out the switches through which DDoS attack flows pass. Based on this, we propose a defense method combined with an enhanced whitelist and a precise dropping strategy, which can effectively mitigate DDoS attacks and minimize the impact on legitimate network traffic. The evaluation results show that our detection method can accurately detect the path that the DDoS attack flows pass through, and can effectively mitigate the DDoS attack. Compared to classic methods, our method improves the detection accuracy by nearly 10%. At the same time, the southbound interface load and CPU overhead brought by our detection and defense process are much lower than the classic methods.

SDN-Defend: A Lightweight Online Attack Detection and Mitigation System for DDoS Attacks in SDN.

With the development of Software Defined Networking (SDN), its security is becoming increasingly important. Since SDN has the characteristics of centralized management and programmable, attackers can easily take advantage of the security vulnerabilities of SDN to carry out distributed denial of service (DDoS) attacks, which will cause the memory of controllers and switches to be occupied, network bandwidth and server resources to be exhausted, affecting the use of normal users. To solve this problem, this paper designs and implements an online attack detection and mitigation SDN defense system. The SDN defense system consists of two modules: anomaly detection module and mitigation module. The anomaly detection model uses a lightweight hybrid deep learning method-Convolutional Neural Network and Extreme Learning Machine (CNN-ELM) for anomaly detection of traffic. The mitigation model uses IP traceback to locate the attacker and effectively filters out abnormal traffic by sending flow rule commands from the controller. Finally, we evaluate the SDN defense system. The experimental results show that the SDN defense system can accurately identify and effectively mitigate DDoS attack flows in real-time.

A Survey of Low Rate DDoS Detection Techniques Based on Machine Learning in Software-Defined Networks

Software-defined networking (SDN) is a new networking paradigm that provides centralized control, programmability, and a global view of topology in the controller. SDN is becoming more popular due to its high audibility, which also raises security and privacy concerns. SDN must be outfitted with the best security scheme to counter the evolving security attacks. A Distributed Denial-of-Service (DDoS) attack is a network attack that floods network links with illegitimate data using high-rate packet transmission. Illegitimate data traffic can overload network links, causing legitimate data to be dropped and network services to be unavailable. Low-rate Distributed Denial-of-Service (LDDoS) is a recent evolution of DDoS attack that has been emerged as one of the most serious vulnerabilities for the Internet, cloud computing platforms, the Internet of Things (IoT), and large data centers. Moreover, LDDoS attacks are more challenging to detect because this attack sends a large amount of illegitimate data that are disguised as legitimate traffic. Thus, traditional security mechanisms such as symmetric/asymmetric detection schemes that have been proposed to protect SDN from DDoS attacks may not be suitable or inefficient for detecting LDDoS attacks. Therefore, more research studies are needed in this domain. There are several survey papers addressing the detection mechanisms of DDoS attacks in SDN, but these studies have focused mainly on high-rate DDoS attacks. Alternatively, in this paper, we present an extensive survey of different detection mechanisms proposed to protect the SDN from LDDoS attacks using machine learning approaches. Our survey describes vulnerability issues in all layers of the SDN architecture that LDDoS attacks can exploit. Current challenges and future directions are also discussed. The survey can be used by researchers to explore and develop innovative and efficient techniques to enhance SDN’s protection against LDDoS attacks.

Detecting flooding DDoS attacks in software defined networks using supervised learning techniques

For the easy and flexible management of large scale networks, Software-Defined Networking (SDN) is a strong candidate technology that offers centralisation and programmable interfaces for making complex decisions in a dynamic and seamless manner. On the one hand, there are opportunities for individuals and businesses to build and improve services and applications based on their requirements in the SDN. On the other hand, SDN poses a new array of privacy and security threats, such as Distributed Denial of Service (DDoS) attacks. For detecting and mitigating potential threats, Machine Learning (ML) is an effective approach that has a quick response to anomalies. In this article, we analyse and compare the performance, using different ML techniques, to detect DDoS attacks in SDN, where both experimental datasets and self-generated traffic data are evaluated. Moreover, we propose a simple supervised learning (SL) model to detect flooding DDoS attacks against the SDN controller via the fluctuation of flows. By dividing a test round into multiple pieces, the statistics within each time slot reflects the variation of network behaviours. And this ”trend” can be recruited as samples to train a predictor to understand the network status, as well as to detect DDoS attacks. We verify the outcome through simulations and measurements over a real testbed. Our main goal is to find a lightweight SL model to detect DDoS attacks with data and features that can be easily obtained. Our results show that SL is able to detect DDoS attacks with a single feature. The performance of the analysed SL algorithms is influenced by the size of training set and parameters used. The accuracy of prediction using the same SL model could be entirely different depending on the training set.

DDoS Attack Detection System Based on RF-SVM-IL Model Under SDN

The data forwarding plane in the Software Defined Network (SDN) is decoupled from the network control plane, which can realize the unified control of the whole network by the controller. Although centralize control provide great convenient in many cases, it is vulnerable to malicious attacks, especially for one of the most threatening attack - Distributed Denial of Service (DDoS). We innovatively propose a machine learning hybrid DDoS attack detection model which provide high precision within short-term. We named our model as RF-SVM-IL, which represents an integration of integrates Random Forest (RF), Support Vector Machine (SVM) and Incremental Learning (IL). The combination of RF and SVM can detect detect attacks in two layers and filter out the easily misclassified samples. Then IL is added to filter new samples to avoid repeated iteration training, and improve the adaptability of the model to dynamic data. Compared with other methods, RF-SVM-IL can detect DDoS attacks in SDN with higher accuracy and shorter time. The experimental results show that the average detection accuracy of RF-SVM-IL model is as high as 98.54%, and the detection time is as low as 2.386s.

DoS and DDoS attacks in Software Defined Networks: A survey of existing solutions and research challenges

Software Defined Networking (SDN) is a new networking paradigm where forwarding hardware is decoupled from control decisions. It promises to dramatically simplify network management and enable innovation and evolution. In SDN, network intelligence is logically centralized in software-based controllers (the control plane), while network devices (OpenFlow Switches) become simple packet-forwarding devices (the data plane) that can be programmed via an open interface (OpenFlow protocol). Such decoupling of the control plane from the data plane introduces various challenges that include security, reliability, load balancing, and traffic engineering. Dreadful security challenges in SDNs are denial of service (DoS) and distributed denial of service (DDoS) attacks. For instance, in SDNs, DoS/DDoS attacks could flood the control plane, the data plane, or the communication channel. Attacking the control plane could result in failure of the entire network, while attacking the data plane or the communication channel results in packet drop and network unavailability. In this paper we deliver several contributions that shed light on the field of DoS/DDoS attacks in SDNs, providing a complete background about the area, including attacks and analysis of the existing solutions. In particular, our contributions can be summarized as follow: we review and systematize the state-of-the-art solutions that address both DoS and DDoS attacks in SDNs through the lenses of intrinsic and extrinsic approaches. Moreover, the discussed countermeasures are organized accordingly to their focus, be it on detection, mitigation, prevention, or graceful degradation. Further, we survey the different approaches and tools adopted to implement the revised solutions. Finally, we also highlight possible future research directions to address DoS/DDoS attacks in SDNs.

Machine Learning for Securing SDN based 5G Network

The fifth-generation (5G) network supports many systems such as reliable communication in potential applications that require maximum security. Advancement in Software-Defined Networking (SDN) is growing with the emerging network architectures targeted from many servers with the various types of Distributed Denial of Service (DDoS) attackers. When malicious users send DDoS attacks, the SDN based 5G networks face security problems and challenges. Despite the security solutions for preventing DDoS attacks in SDN, securing the SDN controller is one of the challenging problems. The purpose of this research is to analyze the suitable machine learning (ML) for securing the SDN controller targeted by DDoS attacks. This paper proposes a security scheme that includes the ML algorithm, adaptive bandwidth mechanism, and dynamic threshold technique. Therefore, the main focus is on the mitigation scheme of DDoS attacks considered in SDN controller through the ML trained model. In this scheme, the proposed approach uses the best ML as a method for finding security solutions that enhance the security of the SDN controller and network performance. In this method, the Extreme Gradient Boosting (XGBoost) and other ML algorithms were used, which not only enhance the accuracy of the security solutions but also improve the overall network performance.

Analysis of Machine Learning for Securing Software-Defined Networking

Advancements in Software-Defined Networking (SDN) are growing with the emerging network architectures which face the security issues and challenges when adversary launches severe attacks such as Distributed Denial of Service (DDoS) attacks. Therefore, a considerable number of solutions have been devised to alleviate DDoS attacks in SDN using a machine learning approach. Thus, the aim of this study is to review and analyze the machine learning-based schemes for securing the SDN environment targeted by DDoS attacks. The schemes’ method, performance metrics, datasets, and other remarks such as benchmarks, strengths, and weaknesses are discussed. The CIC-DDoS 2019 dataset was utilized to evaluate the performance of a set of classification algorithms that are widely used in machine learning-based DDoS attack detection in the SDN environment. Finally, challenges and future directions in the development of machine learning-based detection schemes in SDN are discussed.

The DDoS attacks detection through machine learning and statistical methods in SDN

The distributed denial-of-service (DDoS) attack is a security challenge for the software-defined network (SDN). The different limitations of the existing DDoS detection methods include the dependency on the network topology, not being able to detect all DDoS attacks, applying outdated and invalid datasets and the need for powerful and costly hardware infrastructure. Applying static thresholds and their dependency on old data in previous periods reduces their flexibility for new attacks and increases the attack detection time. A new method detects DDoS attacks in SDN. This method consists of the three collector, entropy-based and classification sections. The experimental results obtained by applying the UNB-ISCX, CTU-13 and ISOT datasets indicate that this method outperforms its counterparts in terms of accuracy in detecting DDoS attacks in SDN.

Comparison of machine learning algorithms for DDoS attack detection in SDN

Introduction: Distributed denial-of-service (DDoS) has become a common attack type in cyber security. Apart from the conventional DDoS attacks, software-defined networks also face some other typical DDoS attacks, such as flow-table attack or controller attack. One of the most recent solutions to detect a DDoS attack is using machine learning algorithms to classify the traffic. Purpose: Analysis of applying machine learning algorithms in order to prevent DDoS attacks in software-defined network. Results: A comparison of six algorithms (random forest, decision tree, naive Bayes, support vector machine, multilayer perceptron, k-nearest neighbors) with accuracy and process time as the criteria has shown that a decision tree and naïve Bayes are the most suitable algorithms for DDoS attack detection. As compared to other algorithms, they have higher accuracy, faster processing time and lower resource consumption. The main features that identify malicious traffic compared to normal one are the number of bytes in a flow, time flow, Ethernet source address, and Ethernet destination address. A flow-table attack can be detected easier than a bandwidth attack, as all the six algorithms can predict this type with a high accuracy. Practical relevance: Important features which play a supporting role in correct data classification facilitate the development of a DDoS protection system with a smaller dataset, focusing only on the necessary data. The algorithms more suitable for machine learning can help us to detect DDoS attacks in software-defined networks more accurately.

Detecting DDoS Attacks in Software-Defined Networks Through Feature Selection Methods and Machine Learning Models

Software Defined Networking (SDN) offers several advantages such as manageability, scaling, and improved performance. However, SDN involves specific security problems, especially if its controller is defenseless against Distributed Denial of Service (DDoS) attacks. The process and communication capacity of the controller is overloaded when DDoS attacks occur against the SDN controller. Consequently, as a result of the unnecessary flow produced by the controller for the attack packets, the capacity of the switch flow table becomes full, leading the network performance to decline to a critical threshold. In this study, DDoS attacks in SDN were detected using machine learning-based models. First, specific features were obtained from SDN for the dataset in normal conditions and under DDoS attack traffic. Then, a new dataset was created using feature selection methods on the existing dataset. Feature selection methods were preferred to simplify the models, facilitate their interpretation, and provide a shorter training time. Both datasets, created with and without feature selection methods, were trained and tested with Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Network (ANN), and K-Nearest Neighbors (KNN) classification models. The test results showed that the use of the wrapper feature selection with a KNN classifier achieved the highest accuracy rate (98.3%) in DDoS attack detection. The results suggest that machine learning and feature selection algorithms can achieve better results in the detection of DDoS attacks in SDN with promising reductions in processing loads and times.

Bandwidth Control Mechanism and Extreme Gradient Boosting Algorithm for Protecting Software-Defined Networks Against DDoS Attacks

Software-Defined Networking (SDN) is an emerging network architecture that addresses the limitation of the traditional network by providing centralized management through a central controller that decouples the control and data planes. However, this development has made the controller a severe target for malicious users to execute attacks such as Distributed Denial of Service (DDoS) attacks. Several schemes have been proposed to mitigate DDoS attacks in SDN, but the challenges still exist. This paper proposes a DDoS mitigation scheme for SDN to ensure accurate attack detection and efficient network resource utilization. The scheme employs two stages: a bandwidth control mechanism and Extreme Gradient Boosting (XGBoost) Algorithm. The bandwidth control mechanism utilizes an adaptive bandwidth profile-based threshold and bandwidth control algorithm that trigger the XGBoost algorithm in case of threshold violations. The use of multiple bandwidth profiles in stetting the threshold ensures the threshold's adaptivity to consider the network traffic variation and reduce the packets drop ratio, which shows an outstanding result. The XGBoost algorithm classifies network traffic flow that violates a set threshold into normal or abnormal traffic. We evaluated the performance of our scheme using CICDDoS2019, NSL-KDD, and CAIDA datasets. Furthermore, we validated our proposed solution in real-time with the SDN environment. The results obtained show that our scheme protects SDN against DDoS attacks with high accuracy, low error, and efficient utilization of the network resources. The proposed system achieved 99.9% accuracy in detecting DDoS attacks with a low false-positive rate of 0.0002% in SDN.

Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges

Distributed denial of service (DDoS) attacks in cloud computing environments are growing due to the essential characteristics of cloud computing. With recent advances in software-defined networking (SDN), SDN-based cloud brings us new chances to defeat DDoS attacks in cloud computing environments. Nevertheless, there is a contradictory relationship between SDN and DDoS attacks. On one hand, the capabilities of SDN, including software-based traffic analysis, centralized control, global view of the network, dynamic updating of forwarding rules, make it easier to detect and react to DDoS attacks. On the other hand, the security of SDN itself remains to be addressed, and potential DDoS vulnerabilities exist across SDN platforms. In this paper, we discuss the new trends and characteristics of DDoS attacks in cloud computing, and provide a comprehensive survey of defense mechanisms against DDoS attacks using SDN. In addition, we review the studies about launching DDoS attacks on SDN, as well as the methods against DDoS attacks in SDN. To the best of our knowledge, the contradictory relationship between SDN and DDoS attacks has not been well addressed in previous works. This work can help to understand how to make full use of SDN's advantages to defeat DDoS attacks in cloud computing environments and how to prevent SDN itself from becoming a victim of DDoS attacks, which are important for the smooth evolution of SDN-based cloud without the distraction of DDoS attacks.

Distributed denial of service attacks in software-defined networking with cloud computing

Although software-defined networking (SDN) brings numerous benefits by decoupling the control plane from the data plane, there is a contradictory relationship between SDN and distributed denial-of-service (DDoS) attacks. On one hand, the capabilities of SDN make it easy to detect and to react to DDoS attacks. On the other hand, the separation of the control plane from the data plane of SDN introduces new attacks. Consequently, SDN itself may be a target of DDoS attacks. In this paper, we first discuss the new trends and characteristics of DDoS attacks in cloud computing environments. We show that SDN brings us a new chance to defeat DDoS attacks in cloud computing environments, and we summarize good features of SDN in defeating DDoS attacks. Then we review the studies about launching DDoS attacks on SDN and the methods against DDoS attacks in SDN. In addition, we discuss a number of challenges that need to be addressed to mitigate DDoS attached in SDN with cloud computing. This work can help understand how to make full use of SDN's advantages to defeat DDoS attacks in cloud computing environments and how to prevent SDN itself from becoming a victim of DDoS attacks.