Machine Learning in Cybersecurity: Techniques and Challenges

Authors

  • Jasmin Praful Bharadiya Doctor of Philosophy Information Technology, University of the Cumberlands, USA

DOI:

https://doi.org/10.47672/ejt.1486
Abstract views: 2536
PDF downloads: 1735

Keywords:

Security, Machine Learning, Survey, Machine Learning, Intrusion Detection, Spam Cybersecurity.

Abstract

In the computer world, data science is the force behind the recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated and intelligent is to extract patterns or insights related to security incidents from cybersecurity data and construct appropriate data-driven models. Data science, also known as diverse scientific approaches, machine learning techniques, processes, and systems, is the study of actual occurrences via the use of data. Due to its distinctive qualities, such as flexibility, scalability, and the capability to quickly adapt to new and unknowable obstacles, machine learning techniques have been used in many scientific fields. Due to notable advancements in social networks, cloud and web technologies, online banking, mobile environments, smart grids, etc., cyber security is a rapidly expanding sector that requires a lot of attention. Such a broad range of computer security issues have been effectively addressed by various machine learning techniques. This article covers several machine-learning applications in cyber security. Phishing detection, network intrusion detection, keystroke dynamics authentication, cryptography, human interaction proofs, spam detection in social networks, smart meter energy consumption profiling, and security concerns with machine learning techniques themselves are all covered in this study. The methodology involves collecting a large dataset of phishing and legitimate instances, extracting relevant features such as email headers, content, and URLs, and training a machine-learning model using supervised learning algorithms. Machine learning models can effectively identify phishing emails and websites with high accuracy and low false positive rates. To enhance phishing detection, it is recommended to continuously update the training dataset to include new phishing techniques and to employ ensemble methods that combine multiple machine learning models for better performance.

 

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Author Biography

Jasmin Praful Bharadiya, Doctor of Philosophy Information Technology, University of the Cumberlands, USA

 

 

References

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Published

2023-06-02

How to Cite

Bharadiya, J. . (2023). Machine Learning in Cybersecurity: Techniques and Challenges. European Journal of Technology, 7(2), 1 - 14. https://doi.org/10.47672/ejt.1486

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