Machine Learning and artificial intelligence algorithms in the detection of cyber-attack patterns [Algoritmos de Machine Learning e inteligencia artificial en la detección de patrones de ciberataques]
DOI:
https://doi.org/10.62574/rmpi.v5iTecnologia.276Keywords:
choice of technology, technology assessment, computer applicationsAbstract
This article aims to evaluate the performance of various machine learning algorithms in the detection of cyber-attacks in the mitigation of threats within networks and computer systems. The methodology was experimental validation and the execution was carried out using the V-cycle computing method. The effectiveness of Machine Learning (ML) algorithms in the detection and mitigation of cyber-attacks was demonstrated, highlighting their ability to identify malicious patterns in network traffic. The results obtained validate the applicability of models such as Support Vector Machines (SVM) and Naive Bayes (NB), which have demonstrated significant performance in threat classification, with SVM being more efficient in detecting complex attacks and NB faster and with lower computational cost. In practical terms, implementing these models in enterprise and government environments could significantly improve cyberattack response capabilities.
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