SAE-CNN-LSTM-based anomaly detection and mitigation framework for cloud-centric SDN environments

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) 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.

In recent years, SDN has evolved as a promising alternative to the traditional networking paradigm. This is because the traditional network is decentralized, making it difficult to manage and provide trouble-shooting. SDN is an approach to network management whereby the forwarding process of the data plane (DP) is disassociated from the control plane (CP). The CP is responsible for decision-making. SDN centrally control the network infrastructure and make it programmable. Communication between the two planes (data and control) is carried out by the OpenFlow (OF) protocol. The deployment of SDN was initiated by Mr. Matrin Casado and his research team in 2008. The original projects developed at Stanford University were SANE and ETHANE. Security reports and web articles suggest that SDN (Software Defined Network) has the caliber to defend DDoS attacks. The year 2017 has been declared as the year of SDN adaption and DDoS mitigation. The initial effort in the direction of compiling the detection and mitigation approaches has been done in (Dayal et al., 2016). Recently a similar effort has been done in (Rochak et al., 2019). The CP of SDN has a complete visibility of the network which makes traffic monitoring feasible. Moreover, SDN open flow switches contain forwarding logic only; decision-making capability is softwarized at 58controller. This helps the controller to instruct and configure the switches with new flow rules which the switches follow. This facilitates the network admins to detect and mitigate these attacks easily. SDN have gained momentum among the possible solutions of DDoS attacks due to its decentralized nature of network management, but solutions come with its repercussions. The centralization and open architecture framework makes SDN difficult for widespread adaptation. The chapter begins with an introduction to DDoS attacks and its classification such as volumetric attack, protocol exploitation attack and application attack. In the next section, we discuss the possible DDoS attacks over SDN framework. We have categorized the vulnerability into three classes: DDoS attack on data, control, and application planes (APs). The DDoS attack on DP can further be classified into TCAM exhaustion, switch DDoS, ICMP flood, TCP flood, TCP_SYN flood, etc. We would discuss Resource depletion, OF bandwidth exhaustion, amplification attacks in DDoS attack over CP. HTTP flooding and Slowloris attack has been discussed in DDoS attack over AP. The next section highlights the working of attack tools used in performing DDoS attacks. Various DDoS tools have been widely exploited to render DDoS attacks and facilitate attackers to carry out dangerous attack on target; here target may be network resources, server resources, or applications. The fourth section focuses on the state-of-the-art for detection and mitigation approaches of DDoS attacks. Detection approaches are classified into statistical, policy, and machine learning-based approaches. DDoS mitigation OF switches, including Alcatel Lucent’s OmniSwitch, Brocade’s MLXe series routers, and applications such as DefensePro by Radware that are available in market; also depend on the statistical threshold to detect the DDoS attack. In addition, this section also incorporates a brief analysis of the previous defense mechanism of the last 10 years and analyzes and compares the products of different security vendors like Cloudflare, Radware, Akamai, Arbor, Nexusguard, etc.

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) revolutionizes network control by separating the control plane from the data plane. Although the latter improves SDN agility and scalability, it creates a security hole, particularly in a central control plane, leading to SDN environments becoming high-profile targets for advanced cybersecurity threats. Due to static and signature-based point-in-time behavior, traditional security methods are unable to keep up with modern attacks that are an anomaly to SDNs. Artificial Intelligence (AI) with its different applications and techniques, has the capability of detecting SDN cyber threats’ anomalies. This paper presents the results of a literature scoping exercise that used a total of 54 papers that looked at AI-driven anomaly detection in SDN. The findings showed that control theory, activity theory, and anomaly detection theory are three theoretical aspects that contribute to the topic of AI-driven anomaly detection in SDN. Furthermore, different machine learning algorithms give different results. In this regard, Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron would help in detecting threats of a familiar nature, while autoencoders and K-means can detect unfamiliar threats. While deep learning architectures such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) support low-latency anomaly detection while maintaining throughput and network stability. The findings could be the basis of providing a conceptual framework on how an intelligent, adaptive, and resilient SDN with real-time threat defense mechanisms could be designed, developed, and deployed.

The use of Software-Defined Networking (SDN) has created a huge leap in the management of computer networks. SDN offers flexibility and the ability to dynamically manage networks, but along with its advantages, it also brings significant challenges in managing quality of service (QoS). QoS is critical to maintaining performance and user experience in increasingly complex and distributed network environments. The research method uses Solution Development to address a quality of service (QoS) management challenge and solution in Software Defined Networking with the approach of designing and developing practical solutions to address QoS management issues and challenges in SDN environments. Control Separation and Data Plane are centralized network control at the SDN controller, while the data plane reside in the networks hardware, both capable of maintaining QoS and a challenges in SDN with a computer network approach that separates the control layer from the data layer, enabling more flexible and centralized network management. Research conclusions related to SDN Software Defined Networking quality of service management problems and solutions, focusing on the problem of separating management and data levels. The separations of control and data plane concept in SDN has great potential to improve flexibility, scalability and more efficient network management.

Software Defined Networking (SDN) is a paradigm for the networks, where the control planes and data planes are separated. It provides centralized network control by separating the network’s control logic from the underlying hardware devices. However, like traditional networks SDN is also susceptible to Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. This paper aims to detect and mitigate DoS and DDoS attacks in an SDN environment using an entropy-based approach. The proposed mechanism calculates the entropy of the network over the collected traffic, and derives a dynamic threshold according to the network traffic conditions to determine whether the environment is subject to DoS or DDoS attacks. In the event of the attack, the proposed mechanism installs a drop flow rule into underlying forwarding devices, discarding the traffic sent from attacking host to victim host

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.

As a new networking paradigm, Software-Defined Networking (SDN) separates data and control planes to facilitate programmable functions and improve the efficiency of packet delivery. Recent studies have shown that there exist various security threats in SDN. For example, a saturation attack may disturb the normal delivery of packets and even make the SDN system out of service by flooding the data plane, the control plane, or both. The existing research has focused on saturation attacks caused by SYN flooding. This paper presents an anomaly detection method, called SA-Detector, for dealing with a family of saturation attacks through IP spoofing, ICMP flooding, UDP flooding, and other types of TCP flooding, in addition to SYN flooding. SA-Detector builds upon the study of self-similarity characteristics of OpenFlow traffic between the control and data planes. Our work has shown that the normal and abnormal traffic flows through the OpenFlow communication channel have different statistical properties. Specifically, normal OpenFlow traffic has a low self-similarity degree whereas the occurrences of saturation attacks typically imply a higher degree of self-similarity. Therefore, SA-Detector exploits statistical results and self-similarity degrees of OpenFlow traffic, measured by Hurst exponents, for anomaly detection. We have evaluated our approach in both physical and simulation SDN environments with various time intervals, network topologies and applications, Internet protocols, and traffic generation tools. For the physical SDN environment, the average accuracy of detection is 97.68% and the average precision is 94.67%. For the simulation environment, the average accuracy is 96.54% and the average precision is 92.06%. In addition, we have compared SA-Detector with the existing saturation attack detection methods in terms of the aforementioned performance metrics and controller's CPU utilization. The experiment results indicate that SA-Detector is effective for the detection of saturation attacks in SDN.

Software defined networking (SDN) technology decouples traditional network architectures to control planes and data planes, providing a new solution for developing new network applications and future Internet technologies. However, with the advent of SDN-related network equipment, security issues have become an important factor restricting its development. Traditional firewall in the face of constantly updated a large number of network attacks loopholes, the urgent need for the firewall to deal with dangerous mechanisms for innovation. This paper presents a SDN firewall architecture that describes its mode of operation and deploys it in the control plane. Simulation shows that the firewall policy has high security in the software defined network environment.

<title>Abstract</title> In today’s digital era, the proliferation of online services has rendered networks increasingly vulnerable to Distributed Denial-of-Service (DDoS) attacks, which can severely disrupt availability and cause significant economic and reputational losses. Software-Defined Networking (SDN), as a promising paradigm for programmable and flexible network management, offers both opportunities and challenges in mitigating such threats. However, the centralized architecture of SDN controllers exposes them to being prime targets for DDoS attacks, demanding advanced detection mechanisms. Traditional detection systems, often signature-based or threshold-based, struggle with novel or complex attack patterns, producing high false positive rates and insufficient detection accuracy. To address these limitations, this paper proposes a hybrid model that integrates Convolutional Neural Networks (CNNs) for feature extraction with Particle Swarm Optimization (PSO) for hyperparameter tuning, tailored for DDoS detection in SDN environments. The CNN leverages deep learning capabilities to automatically capture spatial patterns in traffic data, while PSO optimizes model parameters to enhance detection precision and reduce false alarms. Mathematical formulation of PSO is introduced to optimize CNN hyperparameters, achieving a 0.8% improvement in accuracy over baseline CNN. Experimental evaluations conducted in an SDN testbed using Mininet and the POX controller demonstrate that the proposed CNN-PSO model significantly outperforms baseline CNN and traditional machine learning approaches. The hybrid model achieves higher accuracy, improved F1-scores, and reduced false positive rates, confirming its potential as a scalable and intelligent defense mechanism against evolving DDoS threats in modern network infrastructures.

Software Defined Network (SDN) is a new network architecture that decouples the control plane from the data plane by adding a new entity called “controller”. Most concerns about this new networking trend are: how fast can the controller respond to data path requests and how many data path requests can a controller handle per second. To answer those concerns, this paper presents a study and evaluation of different types of OpenFlow controllers in an SDN environment. The main objective of this study is to perform a comparison between different behaviors of some popular open source SDN controllers such as Floodlight, Beacon, Pox and Ryu in terms of latency and throughput. The main results show that Floodlight is better than the other tested controllers as this paper will explain.

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.

The rapid evolution of digital communication networks has underscored the necessity for intelligent, autonomous systems capable of adapting to dynamic data demands and complex operational environments. This presents a Model for Advancing Network Automation Through Software-Defined and Data-Driven Engineering Solutions, designed to enhance scalability, flexibility, and efficiency in modern network infrastructures. The proposed model integrates Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Artificial Intelligence (AI)-driven analytics to enable real-time monitoring, automated configuration, and predictive optimization of network resources. By decoupling control and data planes, SDN provides centralized programmability and dynamic policy enforcement, while NFV introduces virtualized network functions that minimize hardware dependency and operational costs. The incorporation of Machine Learning (ML) algorithms facilitates data-driven decision-making, enabling automated fault detection, traffic prediction, and self-healing mechanisms that significantly improve network reliability and Quality of Service (QoS). The model also emphasizes data-centric engineering, leveraging big data analytics to continuously refine network performance and optimize bandwidth allocation based on traffic patterns and user behavior. Security is embedded through AI-based intrusion detection systems, encryption protocols, and policy-driven automation frameworks that ensure end-to-end protection. Furthermore, the model promotes energy-efficient operations and sustainable network management through intelligent resource scheduling and adaptive power utilization. Experimental evaluations and simulation results demonstrate substantial improvements in latency reduction, throughput enhancement, and fault tolerance compared to traditional network architectures. This contributes to the advancement of autonomous, self-optimizing digital ecosystems by bridging the gap between programmable networking and intelligent analytics. The proposed model provides a scalable foundation for next-generation applications, including 5G, IoT, cloud computing, and edge networks, ultimately driving the evolution toward fully automated, resilient, and adaptive communication infrastructures.

Arising of IoT(Internet of Things) and Big data, rapid increasing of Information Technology utilization on various fields and spreading of services such as SNS(Social Networking Service) and Cloud Computing led to increase in demand for a various network equipment. device, protocol and policy. This kind of changes needs new generation of network. Correspond with this, the needs for new format of internet is arise and this makes interest in the SDN(Software Defined Network) which is one of the future internet technology. Originally, SDN consist in centralized environment. To solve the problems occurred in this kinds of format, the concept of Multiple Controllers based SDN is appeared. To dealing with the important synchronization issues in Multiple Controllers based SDN, In This paper, we propose an adaptation of the Firefly Algorithm for the Synchronization between Multiple Controllers.