Enhancing SDN Anomaly Detection: A Hybrid Deep Learning Model with SCA-TSO Optimization

The paper explores the evolving landscape of network security, in Software Defined Networking (SDN) highlighting the challenges faced by security measures as networks transition to software-based control. SDN revolutionizes Internet technology by simplifying network management and boosting capabilities through the OpenFlow protocol. It also brings forth security vulnerabilities. To address this we present a hybrid Intrusion Detection System (IDS) tailored for SDN environments leveraging a state of the art dataset optimized for SDN security analysis along with machine learning and deep learning approaches. This comprehensive research incorporates data preprocessing, feature engineering and advanced model development techniques to combat the intricacies of cyber threats in SDN settings. Our approach merges feature from the sine cosine algorithm (SCA) and tuna swarm optimization (TSO) to optimize the fusion of Long Short Term Memory Networks (LSTM) and Convolutional Neural Networks (CNN). By capturing both spatial aspects of network traffic dynamics our model excels at detecting and categorizing cyber threats, including zero-day attacks. Thorough evaluation includes analysis using confusion matrices ROC curves and classification reports to assess the model's ability to differentiate between attack types and normal network behavior. Our research indicates that improving network security using software defined methods can be achieved by implementing learning and machine learning strategies paving the way, for more reliable and effective network administration solutions.