Machine Learning-powered Threat Detection: Mitigating Cybersecurity Challenges

Authors

  • Shailesh Shivaji Deore Associate Professor, Department of Computer Engineering, SSVPS B S DEORE College of Engineering Dhule Maharashtra https://orcid.org/0009-0006-6930-5445
  • Arun Shrirang Pawar Assistant Professor, Bharati Vidyapeeth (Deemed to be University), Institute of Management and Entrepreneurship Development, Pune-411038
  • Prakash Divakaran Professor, Department of Business Administration, Himalayan University Arunachal Pradesh
  • Shivganga C. Maindargi (Assistant Professor- Management Studies), Bharati Vidyapeeth(Deemed to be) University, Pune Abhijit Kadam Institute of Management and Social Sciences, Solapur
  • Sangeeta Paliwal University Librarian, Department- Central Library University and department – Symbiosis International University
  • Vilas S. Gaikwad Associate Professor and HOD, Department of Information Technology, Trinity College of Engineering and Research Pune

Keywords:

Cyber Security, Machine Learning, Detection, Classification

Abstract

In the field of cybersecurity, machine learning-powered threat detection has become a key defence mechanism. This technology offers a ray of hope in an age where digital landscapes are rife with hazards that are constantly developing. This article explores the nuances of using machine learning algorithms to reduce cybersecurity risks.A proactive strategy to security is required given the exponential expansion of data in cyberspace and the sophistication of cyberattacks. Organisations may use machine learning to quickly spot abnormalities and potential dangers because to its capacity to analyse huge datasets and spot trends. It enables threat detection automation, cutting down on response times and lowering the danger of data breaches.Threat detection enabled by machine learning is not without its difficulties, though. This essay examines topics like model robustness, data quality, and the adversarial nature of online attacks. In the context of cybersecurity, it also covers ethical issues and the demand for open and accountable AI systems.This study highlights the enormous potential of machine learning in strengthening cybersecurity defences through a thorough analysis of recent research and real-world applications. It emphasises the value of a coordinated strategy in which the capabilities of machine learning are supplemented by human expertise. Utilising the power of machine learning is crucial for organisations looking to protect their digital assets and data as the cyber threat landscape continues to change.

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Published

29.01.2024

How to Cite

Deore, S. S. ., Pawar, A. S. ., Divakaran, P. ., Maindargi, S. C. ., Paliwal, S. ., & Gaikwad, V. S. . (2024). Machine Learning-powered Threat Detection: Mitigating Cybersecurity Challenges. International Journal of Intelligent Systems and Applications in Engineering, 12(13s), 373–385. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4604

Issue

Vol. 12 No. 13s (2024)

Section

Research Article

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