SDN makes the network programmable, agile, and flexible with data and control traffic separating. This architecture consists of three layers which are application, control and data. The aim of our research is concentrated on the control layer to improve the performance of the network in an autonomic manner. In the first step, we have categorized the performance improvement researches based on network performance improvement solutions proposed in the recent papers. This performance improvement solution clustering is one of our contributions to our paper. The significant contribution in this paper is a novel autonomic SDN-based architecture to ameliorate the performance metrics including blocking probability (BP), delay, jitter, packet loss rate (PLR), and path utilization. Our SDN-based autonomic system consists of three layers (data, autonomic control, and Route learning) to separate the traffics based on deep neural networks (DNN) and to route the flows with the greedy algorithm. The autonomic SDN-based architecture which has proposed in this paper makes better network performance metrics dynamically. Our proposed autonomic architecture will be developed in the POX controller which has developed by python. Mininet is used for simulation and the results are compared with the commonly used SDN named pure SDN in this article. The simulation results show that our structure works better in a full-mesh topology and improves the performance metrics simultaneously. The average performance is improved by about %2. 5 in comparison with pure SDN architecture based on the Area Under Curve (AUC) of network performance.

Software defined network (SDN) is a new architecture for network management and its main concept is centralizing network management in the network control level that has an overview of the network and determines the forwarding rules for switches and routers (the data level). Although this centralized control is the main advantage of SDN, it is also a single point of failure. If this main control is made unreachable for any reason, the architecture of the network is crashed. A distributed denial of service (DDoS) attack is a threat for the SDN controller which can make it unreachable. In the previous researches in DDoS detection in SDN, not enough work has been done on improvement of accuracy in detection. The proposed solution of this research can detect DDoS attack on SDN controller with a noticeable accuracy and prevents serious damage to the controller. For this purpose, fast entropy of each flow is computed at certain time intervals. Then, by the use of adaptive threshold, the possibility of a DDoS attack is investigated. In order to achieve more accuracy, another method, computing flow initiation rate, is used alongside. After observation of the results of this two methods, according to the described conditions, the existence of an attack is confirmed or rejected, or this decision is made at the next step of the algorithm, with further study of flow statistics of network switches by the perceptron neural network. The evaluation results show that the proposed algorithm has been able to make a significant improvement in detection rate and a reduction in false alarm rate compared to closest previous work, besides maintaining the average detection time on an acceptable level.

The extend of the internet across the world has increased cyber-attacks and threats. One of the most significant threats includes denial-of-service (DoS) which causes the server or network not to be able to serve. This attack can be done by distributed nodes in the network as if the nodes collaborated. This attack is called distributed denial-of-service (DDoS). There is offered a novel architecture for the future networks to make them more agile, programmable and flexible. This architecture is called Software defined network (SDN) that the main idea is data and control network flows separation. This architecture allows the network administrator to resist DDoS attacks in the centralized controller. The main issue is to detect DDoS flows in the controller. In this paper, the Self-Organizing Map (SOM) method and Learning Vector Quantization (LVQ) are used for DDoS attack detection in SDN with distributed architecture in the control layer. To evaluate the proposed model, we use a labelled data set to prove the proposed model that has improved the DDoS attack flow detection by 99. 56%. This research can be used by the researchers working on SDN-based DDoS attack detection improvement.

The recent technical trend in the field of communication networks shows a paradigm change from hardware to Software. Software defined networking (SDN) as one of the enablers of digital transformation could have prominent role in this paradigm shift and migration to Knowledge-based network. In this regard, telecom operators are interested in deploying SDN to migrate their infrastructure from a static architecture to a dynamic and programmable platform. However, it seems that they do not consider SDN as one of their priorities and still depend on traditional methods to manage their network (especially in some developing countries such as Iran). Since the first step in applying new technologies is to accept them, we have proposed a comprehensive SDN adoption model with the mixed-method research methodology. At first, the theoretical foundations related to the research problem were examined. Then, based on Grounded theory, in-depth interviews were conducted with 12 experts (including university professors and managers of the major telecom operators). In result, more than a thousand initial codes were determined, which in the review stages and based on semantic commonalities, a total of 112 final codes, 14 categories and 6 themes have been extracted using open, axial and selective coding. Next, in order to confirm the indicators extracted from the qualitative part, the fuzzy Delphi method has been used. In the end, SPSS and SmartPLS 3 Software were used to analyze the data collected from the questionnaire and to evaluate the fit of the model as well as confirm and reject the hypotheses