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Performance Improvement of Machine Learning based Intrusion Detection Systems

Ramin, Shirali Hossein Zadeh | 2017

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 50737 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Jafari Siavoshani, Mahdi
  7. Abstract:
  8. The rapid growth of computer networks has increased the importance of analytics and traffic analysis tools for these networks, and the increasing importance of these networks has increased the importance of security of these networks and the intrusion detection in these networks. Many studies aimed at providing a powerful way to quickly and accurately detect computer network intrusions, each of which has addressed this issue.The common point of all these methods is their reliance on the features extracted from network traffic by an expert. This strong dependence has prevented these methods from being flexible against new attacks and methods of intrusion or changes in the current normal traffic flow, and do not show a good performance. In this research, a method based on recent advances in artificial neural networks and deep learning based on recurrent neural networks has been proposed to analyze network traffic and detect their intrusion. The biggest advantage of this method is to extract features from network traffic to detect intrusion without human agent involvement. This method is with the collection ISCX IDS 2017 Trained and accurately 1.00 was found in the separation of attackers and normal data
  9. Keywords:
  10. Machine Learning ; Deep Learning ; Recurrent Neural Networks ; Intrusion Detection System ; Computer Systems Performance

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