الفهرس 
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Abstract
In recent years, the proliferation of intelligent information systems has led to exponential growth in the volume of data being generated and transmitted across networks. This surge in data has brought about challenges in ensuring efficient and reliable data transmission while also enabling accurate data classification for effective decision-making. This Thesis delves into the realm of enhancing data transmission for intelligent information systems and the integration of Software-Defined Networking (SDN) and machine-learning techniques for data classification. Intelligent information systems, which have recently undergone development and complexity, are now indispensable to the entire world. The networking strategy has unquestionably altered based on machine learning principles to be programable and dynamically configurable with the greatest flexibility and simplicity of use. The term ”software-defined network” (SDN) refers to networks that are managed using software applications and SDN controllers as opposed to the more traditional network management consoles and commands, which require a lot of administrative overhead. To centralize network control and administration, SDN changed the topology of network devices to be more flexible and programable. The software-defined network’s uses protocols for interacting with and managing switches is called OpenFlow (OF). With this protocol, the switches learn the routing information from the controller and then pass data packets based on this information. One of the most important components of the SDN is the controller, which is the smartest component of the network such as the Ryu controller. Including the importance of the Ryu controller in SDN. This thesis discusses how to enhance data traffic transmission and classification in the SDN environment. This thesis shows how can track all data packets and traffic and automatically identify all data types and classify them correctly, so many policies can be applied; security policy, bandwidth, and quota for each type. The most different thing used is using a real SDN network environment and also connecting this Controller to a real physical machine learning server (lambda server) that makes daily continuous training for all data traffic and synchs this at the same time with the SDN controller that applies this instantly on the real live traffic. The controller, using ML, takes the needed action automatically according to the data types. These actions include enhancing security, Data Transmission, and Data Availability in the software-defined networking and Intelligent Systems environment.