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ISSN 2063-5346
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AN ANALYSIS OF THE EFFECTIVENESS OF MACHINE LEARNING ALGORITHMS IN DETECTING AND PREVENTING CYBER-ATTACKS

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Dr. Sreekanth D, Dr. Kurian M J, Jibin N, Sajay K R, Dijesh P
» doi: 10.31838/ecb/2023.12.s3.032

Abstract

Machine learning algorithms have become an important tool for detecting and preventing cyber attacks, due to their ability to identify patterns and anomalies in large and complex datasets. This paper reviews the various machine learning algorithms that have been developed for cyber security, including Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forests, and Deep Learning. The effectiveness of these algorithms in detecting and preventing cyber attacks is evaluated, along with potential limitations and areas for future research. Limitations of machine learning in cyber security include the lack of high-quality and labelled data for training, the lack of interpretability, and the possibility of attackers evading machine learning-based defences. Future research directions include developing more robust machine learning algorithms, improving feature selection methods, developing more sophisticated deep learning models, and integrating human expertise with machine learning algorithms to improve their overall effectiveness.

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