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Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities to Cyber Attacks

Publication Type:

Conference Paper

Authors:

Mohamed Alloghani; Dhiya Al-Jumeily; Abir Hussain; Jamila Mustafina; Thar Baker; Ahmed J. Aljaaf

Source:

Nature-Inspired Computation in Data Mining and Machine Learning. Studies in Computational Intelligence (2020)
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References

Topham, Luke, Kashif Kifayat, Younis A. Younis, Qi Shi, and Bob Askwith. "Cyber Security Teaching and Learning Laboratories: A Survey." Information & Security: An International Journal 35, no. 1 (2016): 51-80.

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APA style: Alloghani, M., Al-Jumeily D., Hussain A., Mustafina J., Baker T., & Aljaaf A. J. (2020).  Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities to Cyber Attacks. Nature-Inspired Computation in Data Mining and Machine Learning. Studies in Computational Intelligence.
Chicago style: Alloghani, Mohamed, Dhiya Al-Jumeily, Abir Hussain, Jamila Mustafina, Thar Baker, and Ahmed J. Aljaaf. "Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities to Cyber Attacks." In Nature-Inspired Computation in Data Mining and Machine Learning. Studies in Computational Intelligence., 2020.
IEEE style: Alloghani, M., D. Al-Jumeily, A. Hussain, J. Mustafina, T. Baker, and A. J. Aljaaf, "Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities to Cyber Attacks", Nature-Inspired Computation in Data Mining and Machine Learning. Studies in Computational Intelligence, 2020.
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