MACHINE LEARNING FOR CYBERSECURITY FOR DETECTING AND PREVENTING CYBER ATTACKS



Authors

  • Prof. Agostino Marengo, Dr. Alessandro Pagano

DOI:

https://doi.org/10.15282/jmes.17.1.2023.10.0759


Keywords:

Machine Learning, Cybersecurity, Detection, Prevention, Anomaly Detection, Intrusion Detection Systems, Predictive Analytics, Real-world Examples, Challenges, Future Directions.


Abstract

This paper delves into the critical role of machine learning (ML) in detecting and preventing cyberattacks on cybersecurity systems. As the threat landscape continues to evolve, traditional security strategies often fall short of protecting against sophisticated adversaries. The advantages of ML techniques provide a powerful solution to this challenge, enabling real-time detection, analysis and mitigation of cyber threats. Diving into key ML concepts and techniques including anomaly detection, intrusion detection systems (IDS), and predictive analytics, this paper illuminates the application of ML in cybersecurity Real-world examples and cases of studied highlights the effectiveness of these techniques to enhance cybersecurity strategies. In addition, the paper addresses the challenges and limitations associated with the use of ML in cybersecurity, such as data quality issues and the emergence of adversarial attacks. Exploring future directions, including the integration of deep learning and AI-powered automation, highlights the ongoing development of ML in cybersecurity Finally, this paper explores the role of ML in cybersecurity in strengthening security positions.



Published

2024-06-14

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