Enhancing Security Measures Through Machine Learning Techniques for DDoS Attack Detection

Abstract

Distributed Denial of Service (DDoS) attacks have become an important threat to internet security with the hasty growth of the global digital population. These attacks aim to overwhelm target systems by flooding them with large network traffic, rendering them inaccessible to legitimate users. To address this challenge, this research paper proposes a machine learning-based approach for detecting and mitigating DDoS attacks. The study investigates the effectiveness of various machine learning techniques, including Long Short-Term Memory (LSTM), Support Vector Machines (SVM), and Logistic Regression, in accurately identifying DDoS attacks within network traffic. The outcomes demonstrate that the LSTM model attains the highest accuracy, with an accuracy score of 99.04%, outperforming traditional methods such as SVM and Logistic Regression. The proposed result leverages the unique capabilities of deep learning to capture complex patterns and long-term