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Machine Learning: Models, Challenges, and Research Directions

Author

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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)

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.

Suggested Citation

  • Tala Talaei Khoei & Naima Kaabouch, 2023. "Machine Learning: Models, Challenges, and Research Directions," Future Internet, MDPI, vol. 15(10), pages 1-29, October.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:10:p:332-:d:1255937
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    References listed on IDEAS

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    1. Muhammad Riaz & Sadiq Ahmad & Irshad Hussain & Muhammad Naeem & Lucian Mihet-Popa, 2022. "Probabilistic Optimization Techniques in Smart Power System," Energies, MDPI, vol. 15(3), pages 1-39, January.
    2. Constantin Waubert de Puiseau & Richard Meyes & Tobias Meisen, 2022. "On reliability of reinforcement learning based production scheduling systems: a comparative survey," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 911-927, April.
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    Cited by:

    1. Hassan Khazane & Mohammed Ridouani & Fatima Salahdine & Naima Kaabouch, 2024. "A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks," Future Internet, MDPI, vol. 16(1), pages 1-42, January.
    2. Bita Ghasemkhani & Kadriye Filiz Balbal & Derya Birant, 2024. "A New Predictive Method for Classification Tasks in Machine Learning: Multi-Class Multi-Label Logistic Model Tree (MMLMT)," Mathematics, MDPI, vol. 12(18), pages 1-27, September.

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