Machine Learning: Models, Challenges, and Research Directions

Tala Talaei Khoei () and Naima Kaabouch
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Tala Talaei Khoei: School of Computer Science and Electrical Engineering, University of North Dakota, Grand Forks, ND 58202, USA
Naima Kaabouch: School of Computer Science and Electrical Engineering, University of North Dakota, Grand Forks, ND 58202, USA

Future Internet, 2023, vol. 15, issue 10, 1-29

Abstract: Machine learning techniques have emerged as a transformative force, revolutionizing various application domains, particularly cybersecurity. The development of optimal machine learning applications requires the integration of multiple processes, such as data pre-processing, model selection, and parameter optimization. While existing surveys have shed light on these techniques, they have mainly focused on specific application domains. A notable gap that exists in current studies is the lack of a comprehensive overview of machine learning architecture and its essential phases in the cybersecurity field. To address this gap, this survey provides a holistic review of current studies in machine learning, covering techniques applicable to any domain. Models are classified into four categories: supervised, semi-supervised, unsupervised, and reinforcement learning. Each of these categories and their models are described. In addition, the survey discusses the current progress related to data pre-processing and hyperparameter tuning techniques. Moreover, this survey identifies and reviews the research gaps and key challenges that the cybersecurity field faces. By analyzing these gaps, we propose some promising research directions for the future. Ultimately, this survey aims to serve as a valuable resource for researchers interested in learning about machine learning, providing them with insights to foster innovation and progress across diverse application domains.

Keywords: artificial intelligence; data pre-processing; machine learning; supervised learning; semi-supervised learning; optimization techniques; reinforcement learning; unsupervised learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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