The Role of Artificial Intelligence in Cybersecurity: Enhancing

Abstract

This study explores the use of AI with increased application of themachine learning approach in improving cybersecurity.Addressing the significance of the four most widespreadalgorithms, CNN, RNN, RF and SVM, this work investigates theeffectiveness of these algorithms in the context of cyber threatsidentification and counteraction. To assess the performance of themodels, the system was validated with a large quantity of datasetswith emphasis on the detection capability, false alarm rate aswell as response time. The findings also show that the proposedCNN model attained the maximum detection accuracy of 96. 5 %,while developing new features the RNN was at 94%. 2%, RF at 91.5% while Naive Bayes is at 87. 7%, Random forest is at 87. 2% andSVM at 88. 9%. The false-positive rates were reported to be atlowest for CNN at 1. 8% more than Urban, thus testifying to itsincreased reliability. Moreover, it took a considerably less amountof time to give the response for CNN which was 0. 5 seconds,compared to 0scaled up for comparison with 5 seconds of readinga text online. 89 seconds for RNN, 1. That is 6 seconds in totalwhile the time taken for RF is 2 seconds, and 1 second for TF. 5seconds for SVM. These studies reaffirm the possibilities of theartificial intelligence and machine learning in improvingcybersecurity through optimized and more precise threatidentification and mitigation means. Subsequent work will bedevoted to continuing the model enhancement works as well as integration with live dataprocessing systems for theincreased effectiveness ofcybersecurity prediction and countermeasures.