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Detecting Distributed Denial of Service attack using ensemble learning

Muttepawar, Atharva (2021) Detecting Distributed Denial of Service attack using ensemble learning. Masters thesis, Dublin, National College of Ireland.

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The Distributed Denial of Service (DDoS) attack is now one of the most used types of attack. As technology evolves, new tools and methods of attacks occur in the picture. As a result of this distributed denial of service attack, detection technologies should improve. This paper is created to show how advanced DDoS can be detected using a machine learning algorithm that can be operated on any hardware. We achieved better accuracy of DDoS attacks using these machine learning techniques. This paper will detect DDoS attacks accurately using four different algorithms and one ensemble technique using the staking method. This detector can detect User Datagram Protocol (UDP) flood, Internet Control Message Protocol (ICMP) flood, Transmission Control Protocol (TCP) flood, and many other forms of DDoS. Previous detectors could identify a limited number of DDoS types or required the usage of many features. Some of the detectors only function with specified procedures. With no predefined protocols, this detector will identify a wide range of DDoS types.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
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: 22 Dec 2022 13:24
Last Modified: 07 Mar 2023 12:50

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