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Summary Software‐defined networking (SDN) is an emerging networking architecture paradigm that decouples the control and data planes. The problem of figuring out the number and positions of controllers and mapping of switches to them is known as the controller placement problem. To provide the resilience against the failure of a controller, each switch is mapped to a primary controller (first reference controller or FRC) and a backup controller (second reference controller or SRC). An existing work aims to minimize the worst‐case latency (WCL) from switch to controller when a controller fails. But this work misses the constraint specifying the definition of a switch's SRC, which might cause an increase in the latency between some switches and their controllers in the event of a controller failure. In order to address this issue, a model is proposed in this paper by incorporating the missing constraint. But the addition of this constraint can potentially cause an increase in the minimum number of required controllers. In order to address this issue, a second model is proposed in this paper by modifying the capacity constraint based on the observation that the capacity of a controller need not be reserved for all the switches for which it acts as SRC. The two proposed models aim at minimizing the WCL from switch to controller when a controller fails. Three network topologies are used to test the proposed models and compare their performance with the existing model in terms of principal and subsidiary metrics. The results demonstrate that the proposed models perform on equal level with the existing model in terms of WCL from switch to SRC while outperforming it in terms of average latency (AL). For example, the first proposed model achieves an average AL reduction of 21.63%, 8.55%, and 25.13% compared with the existing model on three networks. Similarly, the second proposed model achieves an average AL reduction of 21.3%, 8.55%, and 24.19% in each network on three networks. Moreover, the second proposed model achieves a fair trade‐off between the minimum number of controllers required and AL while outperforming both the existing and the first proposed models in terms of the average percentage of reserved controller capacity. [ABSTRACT FROM AUTHOR]

Automotive air conditioning (ACC) system designers faced unique challenges in satisfying customer demands for efficient and comfortable operation across a broad temperature range. The AAC system's effective or fixed temperature setting makes this possible. Many climate control models, like the state flow switching controller in MATLAB control, must handle heating and cooling due to a single Simulink/environment software and frequently contain steady-state error (SSE). The present research proposes to design an automatic temperature control scheme and fuzzy logic control (FLC) for an automotive air-conditioned (AAC) system. In addition, it employs MATLAB Simulink/environment software to model the fuzzy control technique to analyze the AAC system's response. A simulation has been set up to evaluate the suggested system performance to match a selection of user-specified reference temperatures and compressor speeds. Compared with a PID-controlled AAC system, the proposed FLC-based system reduced the undershoot to only 2.30% from 33.30% respectively, and was robust, quicker, and better at controlling temperature. • Challenges in automotive air conditioning systems for designers were addressed. • An automatic temperature control scheme was proposed via fuzzy logic control. • Improved efficiency and comfort in the AAC system was achieved. • The proposed FLC-based system reduced the undershoot to 2.30% from 33.30%. • The FLC-based was more robust, quicker, and better at controlling temperature. [ABSTRACT FROM AUTHOR]

Within the evolving landscape of fifth‐generation (5G) wireless networks, the introduction of network‐slicing protocols has become pivotal, enabling the accommodation of diverse application needs while fortifying defences against potential security breaches. This study endeavours to construct a comprehensive network‐slicing model integrated with an attack detection system within the 5G framework. Leveraging software‐defined networking (SDN) along with deep learning techniques, this approach seeks to fortify security measures while optimizing network performance. This undertaking introduces network slicing predicated on SDN with the OpenFlow protocol and Ryu control technology, complemented by a neural network model for attack detection using deep learning methodologies. Additionally, the proposed convolutional neural networks‐long short‐term memory approach demonstrates superiority over conventional ML algorithms, signifying its potential for real‐time attack detection. Evaluation of the proposed system using a 5G dataset showcases an impressive accuracy of 99%, surpassing previous studies, and affirming the efficacy of the approach. Moreover, network slicing significantly enhances quality of service by segmenting services based on bandwidth. Future research will concentrate on real‐world implementation, encompassing diverse dataset evaluations, and assessing the model's adaptability across varied scenarios. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN) revolutionizes network management by decoupling control plane functionality from data plane devices, enabling the centralized control and programmability of network behavior. This paper uses the ternary system to improve the Central Processing Unit (CPU) inside the SDN controller to enhance network management. The Multiple-Valued Logic (MVL) circuit shows remarkable improvement compared to the binary circuit regarding the chip area, propagation delay, and energy consumption. Moreover, the Carbon Nanotube Field-Effect Transistor (CNTFET) shows improvement compared to other transistor technologies regarding energy efficiency and circuit speed. To the best of our knowledge, this is the first time that a ternary design has been applied inside the CPU of an SDN controller. Earlier studies focused on Ternary Content-Addressable Memory (TCAM) in SDN. This paper proposes a new 1-trit Ternary Full Adder (TFA) to decrease the propagation delay and the Power–Delay Product (PDP). The proposed design is compared to the latest 17 designs, including 15 designs that are 1-trit TFA CNTFET-based, 2-bit binary FA FinFET-based, and 2-bit binary FA CMOS-based, using the HSPICE simulator, to optimize the CPU utilization in SDN environments, thereby enhancing programmability. The results show the success of the proposed design in reducing the propagation delays by over 99% compared to the 2-bit binary FA CMOS-based design, over 78% compared to the 2-bit binary FA FinFET-based design, over 91% compared to the worst-case TFA, and over 49% compared to the best-case TFAs. [ABSTRACT FROM AUTHOR]

Software-Defined Networking is today a mature technology, which is developed in many networks and also embedded in novel architectures like 5G and 6G. The SDN control centralization concept brings significant advantages for management and control in SDN together with the programmability of the data plane. SDN represents a paradigm shift towards agile, efficient, and secure network infrastructures, moving away from traditional, hardware-centric models to embrace dynamic, software-driven paradigms. SDN is compliant also with the virtualization architecture defined in the Network Function Virtualization framework. However, SDN should cooperate seamlessly for some years with the distributed TCP/IP control developed during the years all over the world. Among others, the traditional tasks of routing, forwarding, load balancing, QoS assurance, security, and privacy should be solved. The SDN native centralization brings also some new challenges and problems which are different from the traditional distributed control IP networks. The algorithms and protocols usable in SDN should meet requirements like scalability, convergence, redundancy assurance, sustainability, and good real-time response, and allow orchestrated automation in enhancing network resilience and adaptability. This work presents a theoretical review of state-of-the-art SDN optimization techniques, offering a critical and comparative discussion of various algorithms having tasks such as routing (including dynamic ones), forwarding, load balancing and traffic optimization, and forwarding delay minimization. Attention is pointed to general algorithms which can offer pragmatic solutions for large systems or multiple metric routing. [ABSTRACT FROM AUTHOR]

Sensor networks generate large volumes of data that require robust processing, storage, and analysis capabilities provided by data centers. Software-defined networking, as a new network technique for designing and building networks, provides new concepts and solutions for the current data center system construction. However, the software-defined networking (SDN) framework based on the OpenFlow protocol cannot support new protocols or actively add unknown protocols, since they cause the control plane protocol to become more bloated. It is difficult to solve the problem of network scalability because components are being continuously redesigned. Therefore, an optimized source routing technology is proposed in combination with the protocol-oblivious forwarding technology. Furthermore, a low-cost and extensible network switch is designed to realize the independent data forwarding mechanism that effectively reduces the amount of network control signaling. The experimental results show that the scheme can significantly reduce the scale of flow table items and improve the hardware forwarding rates as well as the flexibility of forwarding rules. Moreover, the design scheme has a good application prospect. [ABSTRACT FROM AUTHOR]

By decoupling the data plane and the control plane of networking devices, Software Defined Network (SDN) enables the control plane devices to obtain the abstract view of the entire network and accomplish network functions as necessary. In SDN, control plane device, known as SDN controller is responsible for managing and setting rules for the data plane devices (SDN switches). Multiple SDN controllers are often used to formulate distributed controller architecture for geographically larger networks where different sets of switches are connected to and managed by different controllers. Due to network dynamics, any particular controller can become overloaded which results in increased response time and thereby reduce the end-user quality of service. To tackle the controller overload issue, in this paper, an effective and efficient load balancing framework is proposed. Most of the previous works in this domain focus on precise load balancing; therefore many load shifting (switch migration) operations are required and consequently degrade the end-user QoS. Additionally, beneath a certain load, CPU performs almost equally well; therefore load balancing before reaching a certain controller's load seems unnecessary and questionable. Unlike most of the previous works, our algorithm balances the load loosely in a proper distributed controller SDN architecture. The load balancing framework uses five key modules to perform faster switch migrations to balance the load. Number of switch migrations, selection time performance, and load balancing precision are the performance metrics for evaluation and comparison for this work. The resultant load-balancing solution has successfully shown its potency in terms of reducing the number of switch migrations and the selection time compared to some recent and notable works in this domain. Moreover, a completely new functionality named Switch Restoration is introduced in our load balancing solution to optimize the network further. [ABSTRACT FROM AUTHOR]

Software-defined networks (SDNs) are expanding their presence beyond laboratories, campus networks, ISPs, and data centre networks, moving into various domains. Although originally designed for campus networks, SDNs face scalability challenges, especially with the use of OpenFlow. Addressing these challenges requires innovative traffic management mechanisms to efficiently handle the growing number of connected devices and the increasing volume of traffic from various types of applications. This article proposes an innovative method for link weight selection that incorporates multipath transmission and flow aggregation in the SDNs. This novel approach improves resource utilization in two key ways. First, it involves the preservation of bandwidth during congestion. Second, it minimizes internal resource usage, as illustrated by a reduction in the number of table entries in switches. Resources undergo optimization through the introduction of a novel mechanism for flow aggregation. This novel mechanism, coupled with multipath transmission, enables adaptive responses to dynamic changes in network conditions. The aggregation process leads to a reduced number of flow entries in the core switches compared to the conventional operation of OpenFlow. The proposed scenarios for link weight allocation allow for a reduction in the number of entries in the core switches by up to 99%. The application of the proposed method also results in an increase of 58% in traffic transmission. [ABSTRACT FROM AUTHOR]

A Multi-Controller Software-Defined Network (MC-SDN) is a revolutionary concept comprising multiple controllers and switches separated using programmable features, enhancing network availability, management, scalability, and performance. The MC-SDN is a potential choice for managing large, heterogeneous, complex industrial networks. Despite the rich operational flexibility of MC-SDN, it is imperative to protect the network deployment with proper protection against potential vulnerabilities that lead to misuse and malicious activities on the MC-SDN structure. The security holes in the MC-SDN structure significantly impact network survivability and performance efficiency. Hence, detecting MC-SDN security attacks is crucial to improving network performance. Accordingly, this work intended to design blockchain-based controller security (BCS) that exploits the advantages of immutable and distributed ledger technology among multiple controllers and securely manages the controller communications against various attacks. Thereby, it enables the controllers to maintain consistent network view and accurate flow tables among themselves and also neglects the controller failure issues. Finally, the experimental results of the proposed BCS approach demonstrated superior performance under various scenarios, such as attack detection, number of attackers, number of controllers, and number of compromised controllers, by applying different performance metrics. [ABSTRACT FROM AUTHOR]

In recent years, with the development of low‐earth orbit broadband satellites, the combination of multi‐path transmission and software‐defined networking (SDN) for satellite networks has seen rapid advancement. The integration of SDN and multi‐path transmission contributes to improving the efficiency of transmission and reducing network congestion. However, the current SDN controllers do not support the multi‐path QUIC protocol (MPQUIC), and the routing algorithm used in current satellite networks based on minimum hop count struggles to meet the real‐time requirements for some applications. Therefore, this paper designs and implements an SDN controller that supports the MPQUIC protocol and proposes a multi‐objective optimization‐based routing algorithm. This algorithm selects paths with lower propagation delays and higher available bandwidth for subflow transmission to improve transmission throughput. Considering the high‐speed mobility of satellite nodes and frequent link switching, this paper also designs a flow table update algorithm based on the predictability of satellite network topology. It enables proactive rerouting upon link switching, ensuring stable transmission. The performance of the proposed solution is evaluated through satellite network simulation environments. The experimental results highlight that SDN‐MPQUIC significantly improves performance metrics: it reduces average completion time by 37.3% to 59.3% compared to QSMPS and by 52.8% to 72.4% compared to Disjoint for files with different sizes. Additionally, SDN‐MPQUIC achieves an average throughput improvement of 81.4% compared to QSMPS and 147.8% compared to Disjoint, while demonstrating a 26.3% lower retransmission rate than QSMPS. [ABSTRACT FROM AUTHOR]

Software Defined Networking (SDN) is a paradigm shift in the network industry with decoupling of control and data plane. This helps network engineers to control and manage their network from centralized controller and troubleshoot the network at much better pace. With the benefits that SDN provides, it also has some vulnerable to attacks like Distributed Denial of Service (DDoS) that can disrupt the availability of the network. Most of the recent research work in DDoS detection and mitigation is done over Traditional network environments and packet limit strategy is used for mitigation by researchers with very less focus on redirection of illegitimate traffic which can result in less downtime and helpful in network analysis. The major contribution of this paper is detection and mitigation of DDoS in Software Defined Networks. We have created our own DDoS dataset with over 1.7 million entries. For Detection, we have used two different methods: (1) Snort (IDS) and (2) Machine Learning. In Machine Learning, we have used eight different Machine Learning algorithms that also include Ensemble Classifier and a Hybrid method of SVM-RF. We were able to get 99.1% accuracy which is a substantial growth as compared to recent works. For Mitigation, we have again used two methods: (1) Drop illegitimate traffic and (2) Redirect Illegitimate traffic. Results showed that hybrid SVM-RF algorithm works better as compared to individual Machine Learning algorithms and also as mitigation depends on Detection accuracy, so, the better the detection, the better the mitigation. In this paper detection and mitigation of DDoS has been performed over a SDN network. In which We have created our own dataset with over 1.7 million entries. For detection we have used two method: Snort (IDS), and Machine Learning and for Mitigation, Drop illegitimate traffic, and Redirect Illegitimate traffic. [ABSTRACT FROM AUTHOR]

Nonlinear loads, crucial components of power system grids, pose a challenge due to harmonics injection. This work tackles this issue with a novel modified green plug–switched filter compensation scheme using fuzzy logic controllers. This innovative scheme presented in this paper utilizes dual action pulse width modulation to ensure switching functions from harmonics reduction and capacitive compensation for inrush nonlinear-type AC loads. The scheme's multi-loop regulations and online switching effectively handle dynamic-type slow-acting inrush, motorized- and other rectifier-type nonlinear loads, enhancing the power factor, power quality at source and load buses, and reducing the total harmonics distortion at the key source and sensitive nonlinear load buses. A simulation model in the MATLAB/SIMULINK-2023b software environment demonstrates the efficiency of the proposed FACTS technique. The modulated dual mode switched filter-capacitive compensation scheme controlled by a fuzzy logic controller ensures less harmonics distortion and improved voltage stabilization. The results show that voltage, current, active power, reactive power, power factor regulation, and effective energy utilization are achievable with the designed Flexible AC Transmission System-Modulated Filter Capacitor Compensation–Switched Filter Compensator (FACTS-MFCC-SFC). The switched modulated AC green plug filter significantly improves power quality and enhances power factor in cases of inrush and nonlinear loads. [ABSTRACT FROM AUTHOR]

Network traffic classification is a fundamental and intricate component of network management in the modern, high-tech era of 5G architectural design, planning of resources, and other areas. Investigation of traffic classification is a key responsibility of traffic engineering in SDN. SDN is a network programmability technology used in 5G networks that divides the control plane from the data plane. It also points the way for autonomous and dynamic network control. SDN needs data from the classification system's flow statistics to apply the appropriate network flow policies. To control the volume of heterogeneous network traffic data in 5G network service, the network administrator must implement a carefully supervised traffic investigation system. This study uses machine learning techniques to examine alternative ways of handling heterogeneous network traffic. The suggested approach is Ensemble Learning for Automated Network Traffic Categorization. i.e., CatBoosting for Automated network traffic classification for multiclass (Cat-ANTC) predicts traffic categorization and offers a higher prediction accuracy than individual models and a more regularized model formalization to decrease over-fitting and boost efficiency. The Cat-ANTC is evaluated using benchmark network traffic datasets that are openly accessible and contrasted with current classifiers and optimization methods. It is clear that when compared to the currently used ensemble techniques, the suggested ensemble methodology produces promising outcomes. Additionally, the proposed method is tested and shown to perform better than the classification of traffic flow using the current model. [ABSTRACT FROM AUTHOR]

The imbalanced I/O load on large parallel file systems affects the parallel I/O performance of high-performance computing (HPC) applications. One of the main reasons for I/O imbalances is the lack of a global view of system-wide resource consumption. While approaches to address the problem already exist, the diversity of HPC workloads combined with different file striping patterns prevents widespread adoption of these approaches. In addition, load-balancing techniques should be transparent to client applications. To address these issues, we propose Tarazu, an end-to-end control plane where clients transparently and adaptively write to a set of selected I/O servers to achieve balanced data placement. Our control plane leverages real-time load statistics for global data placement on distributed storage servers, while our design model employs trace-based optimization techniques to minimize latency for I/O load requests between clients and servers and to handle multiple striping patterns in files. We evaluate our proposed system on an experimental cluster for two common use cases: the synthetic I/O benchmark IOR and the scientific application I/O kernel HACC-I/O. We also use a discrete-time simulator with real HPC application traces from emerging workloads running on the Summit supercomputer to validate the effectiveness and scalability of Tarazu in large-scale storage environments. The results show improvements in load balancing and read performance of up to 33% and 43%, respectively, compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

Summary: Software defined networking is a modern paradigm that divides the control plane from the data plane for improved network manageability. A flow table in the data plane has limited and expensive memory called TCAM. The presence of unwanted flow rules would lead to flow bloat conditions and make the lookup operation inefficient. Eviction schemes based on LRU policy have been widely studied in the literature which preempts the life of the least recently used flow rule and reduces the occupancy of the flow table. LRU considers the past behavior for the eviction of a flow rule. This paper proposes a novel policy that preempts a flow rule by considering its future characteristic, the shortest remaining time (SRT). A rule with a higher probability of being used is avoided from eviction to mitigate the degradation of performance. The modeling of the SRT technique exhibits better utilization of a flow rule that has higher probability of being used. On the other hand, LRU does not guarantee that the evicted flow rule will not be used frequently in the future and it has been shown that an incorrectly evicted flow rule incurs controller delay. The experimental results show that for different traffic rates, SRT has reduced the delay by 15%, reinstallation count by 25%, and jitter by 40%. SRT has increased utilization by 22% compared to LRU. [ABSTRACT FROM AUTHOR]

The rapid development of the vehicle network has brought new challenges to the management for the internet of vehicles (IoV). As a combination of the IoV and software-defined networks (SDN), the software-defined internet of vehicles can provide an efficient network management method and effectively cope with the heterogeneity and high mobility of the IoV. Although the use of a distributed control plane to build a software-defined internet of vehicles is more suitable for a distributed architecture of the IoV, for a time-sensitive network such as the internet of vehicles, the choice of edge control plane also needs to consider factors such as flexibility and reliability. Delay optimization caused by controller plane improvement, deployment cost and possible management overhead is all problems that need to be paid attention to in software-defined internet of vehicles deployment. In view of the above problems, this paper selects the roadside unit (RSU) plane to construct the edge control plane of the software-defined internet of vehicles. The initial deployment of the control plane is first achieved by defining the RSU controller priority. The designed model mainly includes three factors: node contact time, flow scale and node popularity. Secondly, frequent fluctuations of the control plane are not conducive to network management, so the placement cost function and the RSU plane fluctuation cost function are designed to trade off the control plane dynamic adjustment overhead, thereby realizing the dynamic management of the control plane. Simulation results demonstrate that the proposed scheme outperforms the comparison schemes in four aspects: flow setup delay, end-to-end delay, number of controllers, and management overhead. Under normal operation, the proposed scheme is 20.3, 6.6, 34.1 and 4.9% lower than the best scheme IMABC in four parameters respectively. When the path failure probability pfail = 0.02 exists, the proposed scheme is 19, 7.9, 28.6 and 2.7% lower than the best scheme IMABC in the four parameters, respectively. [ABSTRACT FROM AUTHOR]

Time-Sensitive Networking (TSN) aims to provide deterministic communications over Ethernet. The main characteristics of TSN are bounded latency and very high reliability, thus complying with the strict requirements of industrial communications or automotive applications, to name a couple of examples. In order to achieve this goal, TSN defines several scheduling algorithms, among them the Time-Aware Shaper (TAS), which is based on time slots and Gate Control Lists (GCLs). The configuration of network elements to allocate time slots, paths, and GCLs is laborious, and has to be updated promptly and in a dynamic way, as new data flows arrive or disappear. The IEEE 802.1Qcc standard provides the basis to design a TSN control plane to face these challenges, following the Software-Defined Networking (SDN) paradigm. However, most of the current SDN/TSN control plane solutions are monolithic applications designed to run on dedicated servers, and do not provide the required flexibility to escalate when facing increasing service requests. This work presents μ TSN-CP, an SDN/TSN microservices-based control plane, based on the 802.1Qcc standard. Our architecture leverages the advantages of microservices, enabling the control plane to scale up or down in response to varying workloads dynamically. We achieve enhanced flexibility and resilience by breaking down the control plane into smaller, independent microservices. The performance of μ TSN-CP is evaluated in a real environment with TSN switches, and various integer linear problem solvers, running over different computing platforms. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is a network architecture where the control and data plane are separated. As the network size grows, relying on just one controller can lead to various problems. Thus, in highly scalable networks, multiple controllers are needed. This critical issue of determining the number and placement of controllers is known as the controller placement problem (CPP). In this paper, game theory is used to solve CPP by identifying the optimal number of controllers. Two algorithms, golden eagle optimization (GEO) and grey wolf optimization (GWO), are utilized to find the most efficient mapping between switches and controllers. Since CPP is a discrete problem, GEO and GWO have first been discretized and then hybridized to form a new algorithm called GEWO. This algorithm is used to discover the most efficient mapping between switches and controllers. Additionally, simulated annealing is employed for better local search. The effectiveness of this approach is evaluated using different numbers of controllers on four well-known software-defined networks from the Internet Topology Zoo. The results are compared against various existing algorithms in the field, and it is observed that GEWO outperforms the competition. The findings demonstrate that GEWO reduces load imbalance by 24.07%, decreases end-to-end delay by 20.95%, and lowers average energy consumption by 11.65%. [ABSTRACT FROM AUTHOR]

Featured Application: A potential application of interest involves applications that can tolerate some level of inconsistency, such as routing or load-balancing applications. The Physically Distributed Logically Centralized (PDLC) software-defined network (SDN) control plane is physically dispersed across several controllers with a global network view for performance, scalability, and fault tolerance. This design, providing control applications with a global network view, necessitates network state synchronization among controllers. The amount of inter-controller synchronization can affect the performance and scalability of the system. The absence of standardized communication protocols for East-bound SDN interfaces underscores the need for high-performance communication among diverse SDN controllers to maintain consistent state exchange. An inconsistent controller's network view can significantly impact network effectiveness and application performance, particularly in dynamic networks. This survey paper offers an overview of noteworthy AI and non-AI solutions for PDLC SDN architecture in industry and academia, specifically focusing on their approaches to consistency and synchronization challenges. The suggested PDLC framework achieves an adaptive controller-to-controller synchronization rate in a dynamic network environment. [ABSTRACT FROM AUTHOR]

The work furnished in this paper projects the application of fuzzy-aided tilt-integral-derivative (TID) with filter (TIDF) for regulating the dynamical behavior of the electrical interconnected power system (IPS) during uncertain conditions. The optimal design of the fuzzy TIDF is performed using the efficient soft computing technique of the water cycle algorithm (WCA). The suggested fuzzy TIDF controller performance is tested on the multi-area diverse source realistic (MADSR) IPS (MADSRIPS) subjected to control area 1 with step load disturbance (SLD) of 10%. Moreover, the analysis carried out on MADSRIPS is considered with the communication time delays (CTDs) for the investigation near to realistic environment. The efficacy of fuzzy-aided TIDF is contrasted with reported controllers available in literature, and the simulation analysis is acquired from the framework of MATLAB, which revealed the sovereignty of the suggested controller. Later, the MADSRIPS is operated with the integration of a HVDC line in conjunction with the tie-line to improve the performance. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) provides programmability, manageability, flexibility and efficiency compared to traditional networks. These are owing to the SDN's mutual independence or separation of the control and data planes. Decoupling two planes and the centralised nature of SDN enhance DDoS attack protection by facilitating easy implementation of network device policies. The controller's ability to filter network traffic and detect malicious flows is attributed to its global network view. Control and data plane separation brought numerous benefits, but it also introduced a new challenge in terms of its susceptibility to DDoS attack. DDoS attacks are one of the most severe threats to SDN, where the perpetrator disrupts the services of regular users. Machine learning (ML) and deep learning (DL) have emerged as good solutions compared to statistical or policy-based solutions to detect DDoS attack. We have created a detailed taxonomy of DDoS defense solutions. We have surveyed 260 research articles, of which 132 articles are selected based on ML- and/or DL-based solutions to detect DDoS attack in SDN. We discuss the existing works which have applied feature selection algorithms on the dataset to select the best and optimal features for detecting DDoS attack. We present the features of various DDoS datasets available publicly. We also argue for the need to create SDN-specific datasets and then apply feature selection algorithms that may help in better detection of DDoS attack. Finally, we present the research challenges in SDN security that can help the researchers to carry out further research and develop new methods to secure SDN. [ABSTRACT FROM AUTHOR]

The widespread adoption of software-defined networking (SDN) technology has brought revolutionary changes to network control and management. Compared to traditional networks, SDN enhances security by separating the control plane from the data plane and replacing the traditional network architecture with a more flexible one. However, due to its inherent architectural flaws, SDN still faces new security threats. This paper expounds on the architecture and security of SDN, analyzes the vulnerabilities of SDN architecture, and introduces common distributed denial of service (DDoS) attacks within the SDN architecture. This article also provides a review of the relevant literature on DDoS attack detection and mitigation in the current SDN environment based on the technologies used, including statistical analysis, machine learning, policy-based, and moving target defense techniques. The advantages and disadvantages of these technologies, in terms of deployment difficulty, accuracy, and other factors, are analyzed. Finally, this study summarizes the SDN experimental environment and DDoS attack traffic generators and datasets of the reviewed literature and the limitations of current defense methods and suggests potential future research directions. [ABSTRACT FROM AUTHOR]

Accurately and efficiently predicting elephant flows (elephants) is crucial for optimizing network performance and resource utilization. Current prediction approaches for software-defined networks (SDNs) typically rely on complete traffic and statistics moving from switches to controllers. This leads to an extra control channel bandwidth occupation and network delay. To address this issue, this paper proposes a prediction strategy based on incomplete traffic that is sampled by the timeouts for the installation or reactivation of flow entries. The strategy involves assigning a very short hard timeout ( T i n i t i a l ) to flow entries and then increasing it at a rate of r until flows are identified as elephants or out of their lifespans. Predicted elephants are switched to an idle timeout of 5 s. Logistic regression is used to model elephants based on a complete dataset. Bayesian optimization is then used to tune the trained model T i n i t i a l and r over the incomplete dataset. The process of feature selection, model learning, and optimization is explained. An extensive evaluation shows that the proposed approach can achieve over 90% generalization accuracy over 7 different datasets, including campus, backbone, and the Internet of Things (IoT). Elephants can be correctly predicted for about half of their lifetime. The proposed approach can significantly reduce the controller–switch interaction in campus and IoT networks, although packet completion approaches may need to be applied in networks with a short mean packet inter-arrival time. [ABSTRACT FROM AUTHOR]

5G cellular networks are already more than six times faster than 4G networks, and their packet loss rate, especially in the Internet of Vehicles (IoV), can reach 0.5% in many cases, such as when there is high-speed movement or obstacles nearby. In such high bandwidth and high packet loss network environments, traditional congestion control algorithms, such as CUBIC and bottleneck bandwidth and round-trip propagation time (BBR), have been unable to balance flow fairness and high performance, and their flow rate often takes a long time to converge. We propose a congestion control algorithm based on bottleneck routing feedback using an in-network control mode called bottleneck routing feedback (BRF). We use SDN technology (OpenFlow protocol) to collect network bandwidth information, and BRF controls the data transmission rate of the sender. By adding the bandwidth information of the bottleneck in the option field in the ACK packet, considering the flow fairness and the flow convergence rate, a bandwidth allocation scheme compatible with multiple congestion control algorithms is proposed to ensure the fairness of all flows and make them converge faster. The performance of BRF is evaluated via Mininet. The experimental results show that BRF provides higher bandwidth utilization, faster convergence rate, and fairer bandwidth allocation than existing congestion control algorithms in 5G cellular networks. [ABSTRACT FROM AUTHOR]

Network performance has become a crucial consideration in ensuring a flawless user experience in the era of data-intensive applications and cloud computing. Traditional Networks are not suitable for solving such issues, so Software-defined networking (SDN) has evolved as a game-changing technology, promising network flexibility, scalability, and centralized control. However, some challenges remain open to the research, and one of the most popularly known challenges is controller problem placement (CPP). Latency is also a major consideration in the network. However, implementing low-latency communication in SDN systems remains a difficult issue, especially as networks become more complicated. In the realm of SDN, where centralized control meets distributed data planes, minimizing latency between switches and controllers emerges as a pivotal challenge. This article delves into the critical concept of optimizing controller placement within SDN networks to minimize latency, with a primary focus on considering the switch-controller distance. The proposed work uses a Linear programming-based approach to identify the optimal controller location in the network by considering the switch-controller distance and the latency is calculated. Further, a comparison of the proposed work with a heuristic-based approach is performed which shows that the proposed work performs well in terms of latency. [ABSTRACT FROM AUTHOR]

With the advent of next-generation networks, it is crucial to persist in the research and development of key enabling technologies such as software-defined networking (SDN). This involves assessing prospective network deployments, mechanisms, or ideas; an undertaking performed by both network operators and academia to assess the advantages and limitations of the developed proposals related to programmable networks. In this context, simulators are envisioned as essential tools for replicating experiments, offering the required realism, adaptability, and scalability within a controlled environment. However, current solutions have limitations related to the SDN capabilities and indicators that allow for optimizing network performance, which is crucial for Beyond 5G (B5G) and 6G. To overcome this challenge, we propose SDNSimPy, a Python-based simulation framework built on a discrete event simulator. The proposed simulator features a modular architecture with various functional abstractions related to programmable networks, which have been partitioned into distinct modules to streamline its development and facilitate future extensions. Moreover, SDNSimPy has undergone a verification phase to check its implementation. Results obtained from the simulator reveal a significant distinction in the operation modes (proactive and reactive) with respect to end-to-end delay. This parameter is crucial in Beyond 5G (B5G) services and can impact the quality of service (QoS) of network communications. [ABSTRACT FROM AUTHOR]

Software Defined Networking (SDN) is a new network architecture theoretical framework, which based on the partition of the control plane and the data plane. Some of the important features of SDN are centralized network management, open code, scalability, effective utilization of network resources, effective traffic management, security etc. All these administrative decisions taken by the controller which lies at the top of the data plane. Efficient placing of controllers is essential to enhance the network's reliability, which ultimately improves the performance of the network. Much of the work done in the SDN has been aimed on controller placement problems (CPP) by considering latency and reliability of the network separately whereas few of them acknowledged both latency and reliability simultaneously. We considered both communication latency and reliable communication to solve CPP. To achieve this, we aim to minimize the total average latency of the network with a reliable network model that can work perfectly up to the failure of n-1 controllers out of n deployed. We first formulate an efficient algorithm "Capacitated Controllers Arrangement (CCA)" to define the problem and used nature-based metaheuristic technique, Particle Swarm optimization (PSO) to achieve the solution. By intelligently allocating controllers, this dynamic system minimizes network latency and optimizes switch assignments. Three controllers deliver excellent reliability and latency performance even despite controller breakdowns, according to experiments conducted on the Internet2 OS3E architecture. This novel method not only increases SDN efficiency but also establishes a standard for further improvements in controller placement tactics. The outcome shows that the proposed approach achieves its goal efficiently and in future other multi-objective optimization techniques will be explored to solve reliability aware CPP. [ABSTRACT FROM AUTHOR]

Summary: Software‐defined networking (SDN) is an emerging networking architecture where the network control is physically separated from the forwarding plane. It is dynamic, manageable, cost‐effective, and flexible, which is ideal for today's high‐bandwidth applications. The flow table is the fundamental data structure residing in ternary content addressable memory (TCAM) that provides flow rules for incoming flows. The TCAM is the costliest part of an SDN switch that can store limited flow rules. Hence, it is susceptible to distributed denial of service (DDoS) and more specifically to Transformed DDoS (TDDoS). It targets the flow tables, exhausting its limited resource, and resulting in flow table overflow. During overflow, the controller is incapable of installing new rules to the switch, and the switch function is disabled from the network. In this paper, a novel framework named machine learning‐based overflow prevention (MLOP) was proposed which includes a victim switch was identified using association rule mining algorithm concepts. Then, the fuzzy C‐means (FCM) was applied to classify the attack in the victim switch. In addition, FCM ensemble with an optimizing method called dynamic time warping (DTW) which detects similar patterns in the time window during classification. Finally, an elbow method is used that determines the cluster count for optimizing clusters which increases the attack detection and also increases the holding time with reduced packet loss. The real‐time network traffic datasets were used for simulations, and the results were compared with other state‐of‐the‐art approaches. The experimental results show that MLOP increases the holding time by 15% on average and reduces the packet loss due to flow table Overflow by 22.81% than the other existing approaches. In addition, the throughput is increased in the proposed MLOP from 10% to 40%, and end‐to‐end delay is reduced between the variations of 10% to 70%. [ABSTRACT FROM AUTHOR]

In this paper, the low energy cost synchronization control strategy of Markovian switching complex systems/networks is mainly studied and analyzed through multiple perspectives. Firstly, in order to achieve synchronization of Markovian switching complex networks with low energy cost, a control scheme based on the optimal node selection strategy that does not depend on the network coupling strength is improved, and a finite-time controller with a simpler structure is constructed. Secondly, based on the event-triggered control strategy an effective trigger event is designed to achieve the low energy cost synchronization of Markovian switching complex networks on the basis of reducing the information transmission and interaction between networks. Finally, the two control strategies mentioned in this paper are compared and analyzed from multiple perspectives through numerical simulations to better guide practical engineering. [ABSTRACT FROM AUTHOR]

Rapid growth of network technologies necessitates the evolution and reconfiguration of network policies. The rigid nature of legacy networks is a concern for service providers. This concern leads to the popularity, and wide acceptance of emerging network architecture software-defined networking (SDN). The legacy networking approach is vendor-specific, in the case of the devices required for configuration, which is quite restrictive and cumbersome. SDN has overcome this dependency or limitation by providing the capability of centralized control and programmability. However, the architecture of SDN itself faces various security issues. Among the security threats, distributed denial of service (DDoS) attack in the network is crucially indulged in shuttering the virtue of the organization. It is, however, getting popular as the number of users over the web is increasing staggeringly. In this paper, we have presented a comprehensive review of the articles related to the detection of the AL-DDoS (application layer DDoS) attacks in legacy and SDN approaches. The paper will cover DDoS attacks in legacy networks and SDN and the research protocols used to find related high-quality research articles. We have reviewed 124 related articles to select the most relevant studies. We also present the AL-DDoS attack taxonomy, articles classification based on network approach, testing environment, and datasets. Finally, we have marked the limitations of various proposed techniques in the literature related to our survey topic along with the research gaps for the future reference of researchers. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN) outperforms conventional networks in terms of programmability, management, flexibility, and efficiency. This is because SDN separates the control and data planes. The centralised control of devices aids in the prevention of Distributed Denial of Service (DDoS) attacks. The controller has a larger network perspective and has the ability to filter network traffic in order to detect harmful flows. The separation of the control and data planes provided benefits, but it is vulnerable to DDoS attacks. DDoS assaults are difficult to detect and resist in real-time. This is only possible if appropriate features for attack detection are chosen. We intend to employ feature selection methods such as BORUTA, IRelief, Random Forest, Information Gain and Chi-Square Test to obtain the most relevant features for DDoS detection. Moreover, we have devised a strategy to detect and mitigate DDoS attack using tracebacking approach through ONOS Flood Defender (OFD) Application. The application effectively detects different DDoS attack traffic using XGBoost and Multilayer Perceptron algorithms with 99% accuracy and least testing times without adding unnecessary load to the system and mitigates the attack in approximately 3.2 s using tracebacking approach. We have performed our experiment on four benchmark datasets CIC-DoS 2017, CIC-DDoS 2019, CIC-IDS 2018 and InSDN. We have evaluated the trade-off between detection accuracy and testing time in order to determine the most effective detection model for addressing DDoS attacks on SDN networks. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is a networking architecture with improved efficiency achieved by moving networking decisions from the data plane to provide them critically at the control plane. In a traditional SDN, typically, a single controller is used. However, the complexity of modern networks due to their size and high traffic volume with varied quality of service requirements have introduced high control message communications overhead on the controller. Similarly, the solution found using multiple distributed controllers brings forth the 'controller placement problem' (CPP). Incorporating switch roles in the CPP modelling during network partitioning for controller placement has not been adequately considered by any existing CPP techniques. This article proposes the controller placement algorithm with network partition based on critical switch awareness (CPCSA). CPCSA identifies critical switch in the software defined wide area network (SDWAN) and then partition the network based on the criticality. Subsequently, a controller is assigned to each partition to improve control messages communication overhead, loss, throughput, and flow setup delay. The CPSCSA experimented with real network topologies obtained from the Internet Topology Zoo. Results show that CPCSA has achieved an aggregate reduction in the controller's overhead by 73%, loss by 51%, and latency by 16% while improving throughput by 16% compared to the benchmark algorithms. [ABSTRACT FROM AUTHOR]

In promising data transmission systems, new management solutions can be used, for example, we can divide network elements into network layers or implement network elements in the form of virtual network functions. In promising networks, regional centers are software controllers that control local sensors. All control centers are interconnected through a transport subsystem and are included in one network. Sensors provide monitoring of the ability of various types of traffic. One of the most important tasks of traffic management is its filtering. In promising systems, this problem is solved on network sensors by implementing algorithms for matching traffic information and filtering rules. The article proposes an approach to using the concept of software-configurable networks and virtualization of network functions to implement a traffic management system in promising mobile networks. It is proposed to use switches based on the OpenFlow protocol as network sensors. The article also proposes a method for processing and filtering traffic in transport networks, which is implemented in the form of a set of libraries and drivers for interacting network elements directly with applications, which reduces the search time for the necessary rules on telecommunications equipment. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN), which is used in Industrial Internet of Things, uses a controller as its "network brain" located at the control plane. This uniquely distinguishes it from the traditional networking paradigms because it provides a global view of the entire network. In SDN, the controller can become a single point of failure, which may cause the whole network service to be compromised. Also, data packet transmission between controllers and switches could be impaired by natural disasters, causing hardware malfunctioning or Distributed Denial of Service (DDoS) attacks. Thus, SDN controllers are vulnerable to both hardware and software failures. To overcome this single point of failure in SDN, this paper proposes an attack-aware logical link assignment (AALLA) mathematical model with the ultimate aim of restoring the SDN network by using logical link assignment from switches to the cluster (backup) controllers. We formulate the AALLA model in integer linear programming (ILP), which restores the disrupted SDN network availability by assigning the logical links to the cluster (backup) controllers. More precisely, given a set of switches that are managed by the controller(s), this model simultaneously determines the optimal cost for controllers, links, and switches. [ABSTRACT FROM AUTHOR]

The correct and efficient measurement of security properties is key to the deployment of effective cyberspace protection strategies. In this work, we propose GRAPH4, which is a system that combines different security metrics to design an attack detection approach that leverages the advantages of modern network architectures. GRAPH4 makes use of attack graphs that are generated by the control plane to extract a view of the network components requiring monitoring, which is based on the specific attack that must be detected and on the knowledge of the complete network layout. It enables an efficient distribution of security metrics tasks between the control plane and the data plane. The attack graph is translated into network rules that are subsequently installed in programmable nodes in order to enable alerting and detecting network anomalies at a line rate. By leveraging data plane programmability and security metric scores, GRAPH4 enables timely responses to unforeseen conditions while optimizing resource allocation and enhancing proactive defense. This paper details the architecture of GRAPH4, and it provides an evaluation of the performance gains it can achieve. [ABSTRACT FROM AUTHOR]

The logically centralized network control in Software Defined Networks (SDN) facilitates global network vision, flexible and automatic network control. Protection of this centralized network control is a primary concern in next-generation networks. One important issue considered in this manuscript is the protection of SDN control plane from high-rate Packet-In messages. A simple solution to this problem can be to offload certain control functions onto the data plane devices; however, it takes away the flexibility offered by the OpenFlow-based networks. Therefore, a more comprehensive approach is required to protect SDN controllers from high-rate Packet-In messages. In this paper, we propose a Packet-In filtering mechanism which categorizes the Packet-In messages and forwards only the necessary Packet-In messages to other core controller modules. We have implemented the proposed mechanism in Floodlight SDN controller by extending the core controller module and introduced another Packet-In listener module which exposes the REST APIs to retrieve various types of Packet-In messages from the controller core. The proposed mechanism enhances the performance of Floodlight SDN controller as it reduces the CPU and memory load by dispatching only the necessary Packet-In message updates to the other Packet-In listener modules. [ABSTRACT FROM AUTHOR]

Software defined networking (SDN) is an emerging network paradigm that separates the control plane from data plane and ensures programmable network management. In SDN, the control plane is responsible for decision-making, while packet forwarding is handled by the data plane based on flow entries defined by the control plane. The placement of controllers is an important research issue that significantly impacts the performance of SDN. In this work, we utilize clustering techniques to group networks into multiple clusters and propose an algorithm for optimal controller placement within each cluster. The evaluation involves the use of the Mininet emulator with POX as the SDN controller. By employing the silhouette score, we determine the optimal number of controllers for various topologies. Additionally, to enhance network performance, we employ the meeting point algorithm to calculate the best location for placing the controller within each cluster. The proposed approach is compared with existing works in terms of throughput, delay, and jitter using six topologies from the Internet Zoo dataset. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is a networking approach that separates the network's control plane and data plane to provide better network control. The nature of SDN requires an essential focus on its reliability and availability, which includes failure detection aspects. Several techniques have been proposed to enhance SDN failure detection, including passive and active approaches. However, the Route Inspection (RI) algorithm, an example of the active probing method in failure detection, has scalability issues when deployed in large-scale networks as it generates inefficient and lengthy probe paths. To address this, we propose using Community Detection (CD) algorithms to improve the probe path generation of the RI algorithm, resulting in the 'Failure Detection using Community Detection and probing (FDCD)' framework. With the aim to reduce travel paths, the proposed framework minimises the network topology using CD algorithms to reduce the topology size into smaller communities. Then, the RI algorithm calculates the probe path in the communities instead of the single large topology, thus improving the efficiency of the failure detection mechanism. This paper adopts three CD algorithms: Louvain, Label Propagation (LP), and GirvanNewman (GN). We evaluate the framework using three well-known network topologies, COST266, PIORO40, and GERMANY50, in Mininet to observe the framework's performance. The results indicate that the proposed approach improves the algorithm's scalability against its baseline approach, improving the average probe round trip time by 66.87% and the average path installation time by 2.67%. [ABSTRACT FROM AUTHOR]

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Summary: Software defined network (SDN) is a paradigm that emphasizes the separation of the control plane from the data plane, offering advantages such as flexibility and programmability. However, from a security perspective, SDN also introduces new vulnerabilities due to the communication required between these planes. SYN Flood attacks are typical distributed denial‐of‐service (DDoS) attacks that especially challenge network administrators since they produce a large volume of semi‐open TCP connections to a target, compromising its availability. Most of the current solutions to detect and mitigate these attacks are designed to operate at the control plane, imposing an additional overhead on controller functions. Moreover, traffic‐blocking mechanisms, a widely used alternative to protect network resources, have the drawback of restricting legitimate traffic. This work proposes DataPlane‐ML, an integrated solution to detect and mitigate DDoS attacks on SDN, acting directly in the data plane. DataPlane‐ML uses machine learning techniques for attack detection and a mitigation solution based on the node's reputation to avoid blocking legitimate traffic during an attack. Experimental results show that DataPlane‐ML is ≈26%$$ \approx 26\% $$ faster than statistical‐based solutions for attack detection while presenting better accuracy. Moreover, the DataPlane‐ML mitigation solution can preserve more than 95%$$ 95\% $$ of legitimate traffic during an attack. [ABSTRACT FROM AUTHOR]

Metasurfaces have promising potential to revolutionize a variety of photonic and electronic device technologies. However, metasurfaces that can simultaneously and independently control all electromagnetics (EM) waves' properties, including amplitude, phase, frequency, polarization, and momentum, with high integrability and programmability, are challenging and have not been successfully attempted. Here, we propose and demonstrate a microwave universal metasurface antenna (UMA) capable of dynamically, simultaneously, independently, and precisely manipulating all the constitutive properties of EM waves in a software-defined manner. Our UMA further facilitates the spatial- and time-varying wave properties, leading to more complicated waveform generation, beamforming, and direct information manipulations. In particular, the UMA can directly generate the modulated waveforms carrying digital information that can fundamentally simplify the architecture of information transmitter systems. The proposed UMA with unparalleled EM wave and information manipulation capabilities will spark a surge of applications from next-generation wireless systems, cognitive sensing, and imaging to quantum optics and quantum information science. The authors show a universal metasurface antenna capable of simultaneously and independently controlling all five fundamental properties of electromagnetic waves, including amplitude, phase, frequency, polarization, and momentum. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN) is a new architecture with symmetric/asymmetric network structures that separates the control plane of network devices from the data plane, and a Controller Placement Problem (CPP) is a critical management problem in SDN. The main research content of the CPP is to determine the number and location of controllers placed in a network topology, as well as the connection relationship between controllers and switches. However, traditional CPP solutions based on symmetric/asymmetric structures may not be efficient to meet the increasing requirements of SDN applications. In order to improve the CPP solutions from the viewpoint of hierarchical management, this paper considers the CPP solutions as a multi-objective optimization problem based on symmetric/asymmetric structures in the SDN architecture. Thus, this paper then proposes a multi-controller placement strategy based on an improved Harris Hawks Optimization algorithm. Firstly, the local controller load is limited, and a Sin chaotic map is introduced to initialize the CPP scheme. The total latency of the network, the reliability of the node, the total failure rate of the link and the total placement cost are seriously considered when placing the controllers. Secondly, a Cos nonlinear function is added to the global search. A dynamic adaptive weight factor is used to smooth the switching approach between the global search and the local search, so as to enhance the global search ability. Then, a Cauchy variation perturbation is added to the obtained CPP scheme to strengthen the diversity of CPP schemes, and the CPP scheme with the Pareto front is finally solved. The topology simulation of three real large-scale SDN networks shows that the proposed strategy, based on an improved Harris Hawks Optimization algorithm, has more robust advantages in comparison to other algorithms. [ABSTRACT FROM AUTHOR]

Software-defined network (SDNs) have fundamentally changed network infrastructure by decoupling the data plane and the control plane. This architectural shift rejuvenates the network layer by granting the re-programmability and centralized management of networks which brings about exciting challenges. Although an SDN seems to be a secured network when compared to conventional networks, it is still vulnerable and faces rigorous deployment challenges. Moreover, the bifurcation of data and control planes also opens up new security problems. This systematic literature review (SLR) has formalized the problem by identifying the potential attack scenarios and highlighting the possible vulnerabilities. Eighty-six articles have been selected carefully to formulize the SLR. In this SLR, we have identified major security attacks on SDN planes, including the application plane, control plane, and data plane. Moreover, this research also identifies the approaches used by industry experts and researchers to develop security solutions for SDN planes. In this research, we have introduced an attack taxonomy and proposed a collaborative security model after comprehensively identifying security attacks on SDN planes. Lastly, research gaps, challenges, and future directions are discussed for the deployment of secure SDNs. [ABSTRACT FROM AUTHOR]

Since Software-Defined Networking (SDN) allows apps to interact with network-critical resources at the control plane through northbound interface, people hope that these apps have the same level of trust as the controller. Most researchers use static access control policies to solve this problem. In this paper, we achieve a dynamic access control model called DACAS, which is an implementation of attribute-based access control (ABAC) model in the context of the SDN control plane. We analyze how applications can influence SDN through northbound interface and the security requirements of the permission on mainstream controllers. In addition to the security issues caused by the misuse of sensitive APIs, it is found that the northbound and southbound interfaces share the same bandwidth in the network. Once the bandwidth is saturated with requests from the northbound interface, the southbound interface may lose packets. In addition, the storage space of switches is limited. Malicious applications can occupy the living space of normal flow tables by inserting a large number of redundant flow rules. In order to solve these problems, we use the linear quadratic exponential smoothing method to calculate the threshold of inserting flow entries and the upper limit of access time, which can help us implement dynamic access control scheme. In addition, the existing static access control scheme do not take the dynamic or random behavior of the apps into account, which means they cannot adapt to the changing situation in reality. DACAS achieves fine-grained permission management by designing single-case filters and multi-case filters. The prototype system of DACAS is implemented on Ryu controller. Through feasibility analysis, functional evaluation, performance evaluation and security analysis, we demonstrate the robustness and extensibility of DACAS.The run-time overhead introduced by DACAS is on the order of microseconds, which is about 2 ms, but the flexibility of the system is greatly increased by increasing the context attribute in DACAS. [ABSTRACT FROM AUTHOR]