An Efficient DDOS Attack Detection in Software-Defined Networks Using Enhanced Long Short-Term Memory

Software-defined networking (SDN) offers enhanced control over network infrastructure through a central controller, but it remains vulnerable to security threats like DDoS attacks. This paper proposes an automatic attack detection system for SDN, comprising pre-processing, feature selection using Adaptive Walrus Optimization Algorithm (AWaOA), and classification using the enhanced long short-term memory (ELSTM) model. Pre-processing eliminates redundant and missing data, while feature selection optimizes the important features for classification. The ELSTM model then classifies traffic as malicious or legitimate. Performance evaluation includes metrics like accuracy, precision, and recall to assess the effectiveness of the proposed approach in detecting and mitigating DDoS attacks in SDN environments.