Distributed Approach to detect DDOS attack based on Elephant Herding Optimization and Pipeline Artificial Neural Network

Abstract

Cybersecurity experts widely acknowledge that a Distributed Denial of Service (DDoS) assault poses a grave threat, capable of inflicting substantial financial losses and tarnishing the reputation of enterprises. Conventional detection methods are insufficient for identifying DDoS attacks. Simultaneously, with their vast potential, machine learning solutions play a vital role in this field. This paper presents a distributed approach for identifying distributed denial-of-service threats using the pipeline artificial neural network method, supported by elephant herding optimization for feature selection and extraction. The proposed artificial neural network pipeline-based model for detecting DDoS attacks comprises several key stages: collecting the dataset, preparing the data, implementing a balanced data strategy, selecting relevant features using the swarm optimization method Elephant Herding Optimization (EHO), training the model, testing its performance, and evaluating its effectiveness. Experimental results demonstrate that the proposed approach effectively enhances DDoS detection accuracy while reducing false positives, making it a promising solution for network security. This model demonstrated a remarkably high ability to detect DDoS attacks with a 99% accuracy. Thorough investigations demonstrate that the model is highly skilled in implementing security measures and reducing the risks connected with emerging security threats. The effectiveness of our proposed solution, leveraging a pipeline method in Artificial Neural Network (ANN), is crucial to building a reliable model, which is evident in its ability to deliver effective results in low complexity. The proposed method achieves 99.99% accuracy, 99.80% precision, and a False Positive Rate (FPR) of 0.002%, outperforming recent models. These results demonstrate the model's superior accuracy and robustness in identifying complex attack patterns while minimizing false positives.