Collaborative approach of Detection of DDOS attack on SDN Networks

Selvam, Sankaran (2023) Collaborative approach of Detection of DDOS attack on SDN Networks. Masters thesis, Dublin, National College of Ireland.

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Abstract

The project proposes a collaborative Network Anomaly Detection System using bleeding edge technologies like Software Defined Networking with the cutting-edge system like Snort Intrusion Detection System and Machine Learning. Our objective is to develop a strong and clever system that can recognize and counter potential security risks, particularly Distributed Denial of Service assaults on a network. The integration of the smart SDN controller with the Snort IDS forms the basis of our solution. We allow real time packet analysis and deep network traffic inspection by seamlessly combining these components, giving our system the ability to proactively detect malicious traffic patterns and potential threats. The strategy makes use of capabilities of the SDN controller that dynamically reroute malicious traffic to the Snort IDS for in-depth analysis. When examining the traffic and producing alerts for any unusual behaviour, the Snort IDS, known for its effectiveness and versatility in detecting network intrusions, is crucial. We have created a complex Decision Tree based Machine Learning model to improve the accuracy of anomaly detection. With the help of this ML model, which was trained using historical flow information, our system is able to distinguish between safe and unsafe traffic with astounding accuracy. SDN, Snort IDS and ML model are combined to provide a comprehensive network management system that intelligently responds to ever-changing threats. The proposed system has a capability to effectively distinguishing between legitimate traffic and attack traffic, enabling proactive response and protecting the network infrastructure from potential threats.

The project enables to improve network performance and security by combining SDN, Snort IDS and Machine Learning. The main aims are to protect the dependability and integrity of important network infrastructures from current and future cybersecurity threats by offering an innovative, scalable and adaptable solution.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Jayasekera, Evgeniia UNSPECIFIED
Uncontrolled Keywords:	Software Defined Networking (SDN); Distributed Denial of Service (DDOS); Snort; Machine Learning; Network Traffic
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Tamara Malone
Date Deposited:	05 Nov 2024 15:24
Last Modified:	05 Nov 2024 15:24
URI:	https://norma.ncirl.ie/id/eprint/7149

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