Abstract
With the increasingly growing internal and external attacks on computer systems and online services, cybersecurity has become a vibrant research area. Countering intrusive attacks is a daunting task with no universal magic solution that can successfully handle all scenarios. A variety of machine-learning and computational intelligence techniques have been applied extensively to detect and classify these attacks. However, the effectiveness of these techniques greatly depends on the adopted data preprocessing methods for feature extraction and engineering. This paper presents an extended taxonomy of the work related to intrusion detection and reviews the state-of-the-art techniques for data preprocessing. It offers a critical up-to-date survey which can be an instrumental pedagogy to help junior researchers conceive the vast amount of research work and gain a holistic view and awareness of various contemporary research directions in this domain.
Original language | English |
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Pages (from-to) | 1369-1383 |
Number of pages | 15 |
Journal | Journal of Intelligent and Fuzzy Systems |
Volume | 34 |
Issue number | 3 |
DOIs | |
State | Published - 2018 |
Bibliographical note
Publisher Copyright:© 2018 - IOS Press and the authors. All rights reserved.
Keywords
- Information systems
- computational intelligence
- cybersecurity
- dimensionality reduction
- feature discretization
- feature engineering
- feature normalization
- feature selection
- intrusion detection
- machine learning
ASJC Scopus subject areas
- Statistics and Probability
- General Engineering
- Artificial Intelligence