Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an SVM-based DDoS detection model shows superior performance. This comparative analysis offers a valuable insight into the development of efficient and accurate techniques for detecting DDoS attacks in SDN environments with less complexity and time.

Automated summary

Software-defined networking (SDN) introduces new security and privacy risks, such as distributed denial-of-service (DDoS) attacks. Machine learning (ML) and deep learning (DL) have emerged as effective approaches to quickly identify and mitigate these threats. This research compares various classification methods and finds that support vector machines (SVMs) demonstrate the highest prediction accuracy for DDoS detection in SDN environments. The analysis provides valuable insights for developing efficient and accurate techniques to detect DDoS attacks with less complexity and time.