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Feature selection and intrusion detection in cloud environment based on machine learning algorithms

Javadpour, A ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1109/ISPA/IUCC.2017.00215
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2018
  4. Abstract:
  5. Characteristics and way of behavior of attacks and infiltrators on computer networks are usually very difficult and need an expert. In addition; the advancement of computer networks, the number of attacks and infiltrations is also increasing. In fact, the knowledge coming from expert will lose its value over time and must be updated and made available to the system and this makes the need for expert person always felt. In machine learning techniques, knowledge is extracted from the data itself which has diminished the role of the expert. Various methods used to detect intrusions, such as statistical models, safe system approach, neural networks, etc., all weaken the fact that it uses all the features of an information packet rotating in the network for intrusion detection. Also, the huge volume of information and the unthinkable state space is also an important issue in the detection of intrusion. Therefore, the need for automatic identification of new and suspicious patterns in attempt for intrusion with the use of more efficient methods (Lower cost and higher performance) is needed more than before. The purpose of this study is to provide a new method based on intrusion detection systems and its various architectures aimed at increasing the accuracy of intrusion detection in cloud computing. © 2017 IEEE
  6. Keywords:
  7. Feature selection ; Machine learning ; Neural network ; Artificial intelligence ; Automation ; Computer networks ; Data mining ; Distributed computer systems ; Feature extraction ; Learning algorithms ; Learning systems ; Neural networks ; Ubiquitous computing ; Automatic identification ; Classification algorithm ; Cloud environments ; Information packets ; Intrusion detection systems ; Machine learning techniques ; On-machines ; System approach ; Intrusion detection
  8. Source: Proceedings - 15th IEEE International Symposium on Parallel and Distributed Processing with Applications and 16th IEEE International Conference on Ubiquitous Computing and Communications, ISPA/IUCC 2017 ; 25 May , 2018 , Pages 1417-1421 ; 9781538637906 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/8367445