Abstract :

(DDoS) Attacks Are One Of The Major Cybersecurity Threats That Disrupt Network Services By Overwhelming Servers, Systems, Or Networks With Massive Amounts Of Malicious Traffic. These Attacks Can Cause Service Downtime, Financial Loss, And Reduced System Performance, Affecting Organizations And Users Worldwide. The Proposed DDoS Attack Detection And Mitigation System Uses Artificial Intelligence And Machine Learning Techniques To Identify Abnormal Traffic Patterns And Detect Malicious Activities In Real Time. The System Continuously Monitors Network Traffic, Analyzes Packet Behavior, And Classifies Legitimate And Attack Traffic Using Intelligent Algorithms. Once A DDoS Attack Is Detected, Mitigation Techniques Such As Traffic Filtering, Rate Limiting, IP Blocking, And Load Balancing Are Applied To Reduce The Impact Of The Attack And Maintain Service Availability. The Proposed System Improves Network Security, Enhances Detection Accuracy, Minimizes False Positives, And Provides Fast Response Mechanisms For Protecting Critical Network Infrastructures From Cyber Threats.

Published:

04-6-2026

Issue:

Vol. 26 No. 6 (2026)

Page Nos:

115-120

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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