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Detection of DDOS Attacks in Network Traffic through Clustering based and Machine Learning Classification

Kazim Al Janabi, Ali Hossein | 2021

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 53961 (52)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Peyvandi, Hossein
  7. Abstract:
  8. Today, with the development of technology, cyberattacks are on the rise. Personal and corporate computer systems can be exposed to various threats and dangers of hackers and malware, including information theft, forgery, and denial of service, which can cause great material and moral damage to individuals and organizations. So, it is necessary to take security measures in this regard. Many security mechanisms are available to prevent security vulnerabilities against various threats. In this study, first, after carefully studying network attacks, we identify the criteria for identifying attacks that can be executed in network traffic and explain how to calculate them. The current research introduces an approach that is based on clustering which is used for identifying the network traffic flows. The last is related to both; ordinary traffic and (DDoS) traffic. For the identification of attacks of victim-end, certain features are taken. Monitored at the target machine, three features are employed for the demonstration of this work: The clustering K-means in addition to the highlight extraction beneath Principal Components Analysis (PCA). After labeling data, Decision Trees algorithms are employed to get a prepared model for the future arrangement. Experimental validation results show that an Artificial Neural Network is capable to learn a design for displaying the sequence of connections between computers in a network and can be used to remotely detect network traffic. Dense DDoS attacks are detected more accurately, which ultimately reduces the speed of incorrect classification and increases accuracy
  9. Keywords:
  10. Distributed Denial of Service (DDOS)Attack ; K-means Clustering ; Neural Network ; Decision Making Tree ; K-Nearest Neighbor Method ; Machine Learning-based Classification ; Intrusion Detection System

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