Affiliations: [a] College of Computer Sciences and Engineering, University of Ha’il, Ha’il, Saudi Arabia
| [b] Department of Information and Computer Science, College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Correspondence:
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Corresponding author. El-Sayed M. El-Alfy, Department of Information and Computer Science, College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia. E-mail: alfy@kfupm.edu.sa.
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.
