Machine Learning Assisted Distributed Denial of Service Attack Detection and Mitigation System
Abstract
Distributed Denial of Service (DDoS) attacks represent one of the most disruptive cybersecurity threats targeting modern network infrastructures, cloud platforms, and Internet of Things (IoT) ecosystems. These attacks overwhelm target systems by flooding them with massive volumes of malicious traffic, leading to service degradation or complete system unavailability. Traditional rule-based intrusion detection systems are often inadequate in identifying evolving and sophisticated DDoS attack patterns. This research proposes a Machine Learning (ML) assisted DDoS attack detection and mitigation system designed to enhance real-time threat identification and response capabilities in distributed network environments. The proposed system integrates supervised and unsupervised learning algorithms to analyze network traffic patterns, detect anomalies, and classify malicious activities with high accuracy. Feature extraction techniques are applied to identify key traffic attributes such as packet rate, flow duration, source-destination behavior, and protocol usage. The system also incorporates an adaptive mitigation module that dynamically responds to detected threats by filtering malicious traffic and rerouting legitimate requests. The architecture is evaluated using simulation-based experiments and performance metrics including detection accuracy, false positive rate, response time, and system throughput. The results demonstrate that machine learning-based approaches significantly improve the efficiency, scalability, and reliability of DDoS detection and mitigation systems in distributed computing environments
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 4 No. 3 (2019): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
5467-5475 |
Published |
May 2, 2019 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Margie P (%2019). Machine Learning Assisted Distributed Denial of Service Attack Detection and Mitigation System. International Journal of Emerging Trends in Engineering and Management Research , Vol. 4 No. 3 (2019): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 5467-5475. https://doi.org/10.15662/ijetemr.2019.0403001 |
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