Cybersecurity and Cyber Forensics: Machine Learning Approach Systematic Review

Authors

  • Ibrahim Goni Department of Computer Science, Adamawa State University, Mubi, Nigeria
  • Jerome M. Gumpy Department of Computer Science, Federal University, Gashua, Nigeria
  • Timothy U. Maigari Department of Computer Science, Federal College of Education Gombe, Nigeria
  • Murtala Mohammad Department of Computer Science, Adamawa State University, Mubi, Nigeria

DOI:

https://doi.org/10.30564/ssid.v2i2.2495

Abstract

The proliferation of cloud computing and internet of things has led to the connectivity of states and nations (developed and developing countries) worldwide in which global network provide platform for the connection.Digital forensics is a field of computer security that uses software applications and standard guidelines which support the extraction of evidences from any computer appliances which is perfectly enough for the court of law to use and make a judgment based on the comprehensiveness, authenticity and objectivity of the information obtained. Cybersecurity is of major concerned to the internet users worldwide due to the recent form of attacks,threat, viruses, intrusion among others going on every day among internet of things. However, it is noted that cybersecurity is based on confidentiality,integrity and validity of data. The aim of this work is make a systematic review on the application of machine learning algorithms to cybersecurity and cyber forensics and pave away for further research directions on the application of deep learning, computational intelligence, soft computing to cybersecurity and cyber forensics.

Keywords:

Cybersecurity, Cyber forensics, Cyber space, Cyber threat, Machine learning and deep learning

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How to Cite

Goni, I., Gumpy, J. M., Maigari, T. U., & Mohammad, M. (2020). Cybersecurity and Cyber Forensics: Machine Learning Approach Systematic Review. Semiconductor Science and Information Devices, 2(2), 25–29. https://doi.org/10.30564/ssid.v2i2.2495

Issue

Article Type

Review