Memory-Efficient Multi-Island Jaya Feature Selection for Large-Scale DDoS Attack Detection

Distributed Denial-of-Service (DDoS) attacks are among the most destructive events in the cyber realm, causing significant disruptions to the operations of organizations, communities, and individuals. These severe incidents occur when an attacker exhausts all resources of the targeted system, preventing legitimate users from accessing it. The primary challenges in recognizing these attacks are the memory constraints and scalability issues of current attack detection systems. To detect such large-scale attacks, a thorough analysis of multidimensional volumetric network traffic is conducted. However, the presence of substandard features within this traffic diminishes the effectiveness of detection. This paper introduces a feature selection pipelining that combines a memory-efficient D-optimal subsampling method with the Jaya optimization algorithm, designed to extract important features from the CIC-DDoS2019 dataset. The attack detection model employs this feature selection to create an automated, scalable classifier that uses the H2O Automated Machine Learning (AutoML) platform to identify large-scale DDoS attacks. This classifier effectively differentiates between eight distinct types of attacks and one benign traffic type. The model reports remarkable metrics, achieving a precision of 89. 9%, recall of 90. 0%, f1 score of 89. 9%, and an accuracy of 89. 9% in the cross-validation data. In addition, it outperforms the capabilities of recent models.