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Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

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  • Kamran Shaukat

    (School of Electrical Engineering and Computing, The University of Newcastle, Newcastle 2308, Australia
    Punjab University College of Information Technology, University of the Punjab, Lahore 54590, Pakistan)

  • Suhuai Luo

    (School of Electrical Engineering and Computing, The University of Newcastle, Newcastle 2308, Australia)

  • Vijay Varadharajan

    (School of Electrical Engineering and Computing, The University of Newcastle, Newcastle 2308, Australia)

  • Ibrahim A. Hameed

    (Department of ICT and Natural Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway)

  • Shan Chen

    (School of Electrical Engineering and Computing, The University of Newcastle, Newcastle 2308, Australia)

  • Dongxi Liu

    (Data61, Commonwealth Scientific and Industrial Research Organization, Canberra 3169, Australia)

  • Jiaming Li

    (Data61, Commonwealth Scientific and Industrial Research Organization, Canberra 3169, Australia)

Abstract

Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.

Suggested Citation

  • Kamran Shaukat & Suhuai Luo & Vijay Varadharajan & Ibrahim A. Hameed & Shan Chen & Dongxi Liu & Jiaming Li, 2020. "Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity," Energies, MDPI, vol. 13(10), pages 1-27, May.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2509-:d:358837
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    Citations

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    Cited by:

    1. Fatima Rafiq & Mazhar Javed Awan & Awais Yasin & Haitham Nobanee & Azlan Mohd Zain & Saeed Ali Bahaj, 2022. "Privacy Prevention of Big Data Applications: A Systematic Literature Review," SAGE Open, , vol. 12(2), pages 21582440221, May.
    2. Yuan Wang & Liping Yang & Jun Wu & Zisheng Song & Li Shi, 2022. "Mining Campus Big Data: Prediction of Career Choice Using Interpretable Machine Learning Method," Mathematics, MDPI, vol. 10(8), pages 1-18, April.
    3. Frank Cremer & Barry Sheehan & Michael Fortmann & Arash N. Kia & Martin Mullins & Finbarr Murphy & Stefan Materne, 2022. "Cyber risk and cybersecurity: a systematic review of data availability," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(3), pages 698-736, July.
    4. Nasir, Nida & Kansal, Afreen & Alshaltone, Omar & Barneih, Feras & Shanableh, Abdallah & Al-Shabi, Mohammad & Al Shammaa, Ahmed, 2023. "Deep learning detection of types of water-bodies using optical variables and ensembling," LSE Research Online Documents on Economics 118724, London School of Economics and Political Science, LSE Library.
    5. Wojciech Szczepanik & Marcin Niemiec, 2022. "Heuristic Intrusion Detection Based on Traffic Flow Statistical Analysis," Energies, MDPI, vol. 15(11), pages 1-19, May.
    6. Hail Jung & Jinsu Jeon & Dahui Choi & Jung-Ywn Park, 2021. "Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry," Sustainability, MDPI, vol. 13(8), pages 1-16, April.
    7. Chetna Monga & Deepali Gupta & Devendra Prasad & Sapna Juneja & Ghulam Muhammad & Zulfiqar Ali, 2022. "Sustainable Network by Enhancing Attribute-Based Selection Mechanism Using Lagrange Interpolation," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    8. Feng Wu & Wanqiang Xu & Chaoran Lin & Yanwei Zhang, 2022. "Knowledge Trajectories on Public Crisis Management Research from Massive Literature Text Using Topic-Clustered Evolution Extraction," Mathematics, MDPI, vol. 10(12), pages 1-18, June.
    9. Pengyi Liao & Jun Yan & Jean Michel Sellier & Yongxuan Zhang, 2022. "TADA: A Transferable Domain-Adversarial Training for Smart Grid Intrusion Detection Based on Ensemble Divergence Metrics and Spatiotemporal Features," Energies, MDPI, vol. 15(23), pages 1-18, November.
    10. Ahmed Abdu & Zhengjun Zhai & Redhwan Algabri & Hakim A. Abdo & Kotiba Hamad & Mugahed A. Al-antari, 2022. "Deep Learning-Based Software Defect Prediction via Semantic Key Features of Source Code—Systematic Survey," Mathematics, MDPI, vol. 10(17), pages 1-26, August.

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