Comparative Analysis of Network Security: Firewall, IDS, and AI-Based Defense Against DDoS Attacks

Abstract

Distributed Denial of Service (DDoS) attacks have become one of the most significant threats in today’s network security landscape. By overwhelming a network with excessive traffic, these attacks can disrupt service availability and render digital systems inaccessible. Various defense mechanisms have been developed to counter this threat, including firewalls for initial traffic filtering, Intrusion Detection Systems (IDS) for real-time anomaly detection, and Artificial Intelligence (AI)-based systems that can adaptively recognize new attack patterns. This study aims to analyze and compare the effectiveness of these three approaches in mitigating DDoS attacks. The method used is a literature review of selected scientific journals published between 2019 and 2024. The findings show that firewalls are effective for standard traffic filtering, IDS excels in early threat detection, and AI-based systems offer high accuracy with lower false positive rates. A combined implementation of these three methods is recommended to build a comprehensive and adaptive network defense system capable of withstanding increasingly complex DDoS threats.