Evaluating Predictive Models in Cybersecurity: A Comparative Analysis of Machine and Deep Learning Techniques for Threat Detection
Journal:
arXiv
Published Date:
Jul 8, 2024
Abstract
As these attacks become more and more difficult to see, the need for the
great hi-tech models that detect them is undeniable. This paper examines and
compares various machine learning as well as deep learning models to choose the
most suitable ones for detecting and fighting against cybersecurity risks. The
two datasets are used in the study to assess models like Naive Bayes, SVM,
Random Forest, and deep learning architectures, i.e., VGG16, in the context of
accuracy, precision, recall, and F1-score. Analysis shows that Random Forest
and Extra Trees do better in terms of accuracy though in different aspects of
the dataset characteristics and types of threat. This research not only
emphasizes the strengths and weaknesses of each predictive model but also
addresses the difficulties associated with deploying such technologies in the
real-world environment, such as data dependency and computational demands. The
research findings are targeted at cybersecurity professionals to help them
select appropriate predictive models and configure them to strengthen the
security measures against cyber threats completely.
