IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v15y2023i10p332-d1255937.html
   My bibliography  Save this article

Machine Learning: Models, Challenges, and Research Directions

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/15/10/332/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/15/10/332/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tala Talaei Khoei & Aditi Singh, 2024. "A survey of Emotional Artificial Intelligence and crimes: detection, prediction, challenges and future direction," Journal of Computational Social Science, Springer, vol. 7(3), pages 2359-2402, December.
    2. 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.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fathy, Ahmed, 2023. "Bald eagle search optimizer-based energy management strategy for microgrid with renewable sources and electric vehicles," Applied Energy, Elsevier, vol. 334(C).
    2. Aswad Adib & Joao Onofre Pereira Pinto & Madhu S. Chinthavali, 2023. "GA-Based Voltage Optimization of Distribution Feeder with High-Penetration of DERs Using Megawatt-Scale Units," Energies, MDPI, vol. 16(13), pages 1-10, June.
    3. Rahim, Sahar & Wang, Zhen & Ju, Ping, 2022. "Overview and applications of Robust optimization in the avant-garde energy grid infrastructure: A systematic review," Applied Energy, Elsevier, vol. 319(C).
    4. Moiz Ahmad & Muhammad Babar Ramzan & Muhammad Omair & Muhammad Salman Habib, 2024. "Integrating Risk-Averse and Constrained Reinforcement Learning for Robust Decision-Making in High-Stakes Scenarios," Mathematics, MDPI, vol. 12(13), pages 1-32, June.
    5. Araoye, Timothy Oluwaseun & Ashigwuike, Evans Chinemezu & Mbunwe, Muncho Josephine & Bakinson, Oladipupo Idris & Ozue, ThankGod Izuchukwu, 2024. "Techno-economic modeling and optimal sizing of autonomous hybrid microgrid renewable energy system for rural electrification sustainability using HOMER and grasshopper optimization algorithm," Renewable Energy, Elsevier, vol. 229(C).
    6. Bingqing Xia & Hao Wu & Wenbin Yang & Lu Cao & Yonghua Song, 2022. "Parametric Transient Stability Constrained Optimal Power Flow Solved by Polynomial Approximation Based on the Stochastic Collocation Method," Energies, MDPI, vol. 15(11), pages 1-20, June.
    7. Juseung Choi & Hoyong Eom & Seung-Mook Baek, 2022. "A Wind Power Probabilistic Model Using the Reflection Method and Multi-Kernel Function Kernel Density Estimation," Energies, MDPI, vol. 15(24), pages 1-17, December.
    8. Ibrar Ullah & Irshad Hussain & Khalid Rehman & Piotr Wróblewski & Wojciech Lewicki & Balasubramanian Prabhu Kavin, 2022. "Exploiting the Moth–Flame Optimization Algorithm for Optimal Load Management of the University Campus: A Viable Approach in the Academia Sector," Energies, MDPI, vol. 15(10), pages 1-27, May.
    9. Kabulo Loji & Sachin Sharma & Nomhle Loji & Gulshan Sharma & Pitshou N. Bokoro, 2023. "Operational Issues of Contemporary Distribution Systems: A Review on Recent and Emerging Concerns," Energies, MDPI, vol. 16(4), pages 1-21, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:15:y:2023:i:10:p:332-:d:1255937. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.