International Research Journal of Education and Technology

SDN, or software-defined networking, revolutionizes network infrastructure management and control with programmable centralization. Despite their benefits, SDNs introduce security threats, most notably botnet attacks. Botnets, networks of infected devices controlled by malevolent actors, threaten networked system availability, integrity, and confidentiality. Botnets can launch DDoS, spam, and data exfiltration attacks.

Network botnet mitigation and detection typically use statistical anomaly detection, rule-based systems, or signature-based detection. These tactics have their benefits, but botnet operators' ever-changing strategies can outpace them. These methods may also miss botnet activity detection possibilities by not using SDNs' flow-level and topology data.Deep learning has excelled in computer vision, NLP, and cybersecurity. Deep learning models like RNNs and CNNs can automate many complex tasks, including network intrusion detection. We describe a new SDN strategy that uses deep learning to detect and mitigate botnet attacks. We automatically train discriminative characteristics using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to identify safe and dangerous network traffic activities. These algorithms should be trained on labeled datasets of regular and botnet traffic to detect botnet activities in real time. We also integrate our deep learning-based detection system with SDN controllers to prevent botnet attacks. SDNs' programmability and agility allow dynamic reconfiguration of network policies and routing patterns to combat botnet attacks.