Evaluating Predictive Models in Cybersecurity: A Comparative Analysis of Machine and Deep Learning Techniques for Threat Detection

TL;DR

This paper compares machine learning and deep learning models for cybersecurity threat detection, evaluating their accuracy, strengths, and deployment challenges to guide professionals in selecting effective models.

Contribution

It provides a comprehensive comparison of various models like Random Forest, SVM, and VGG16 for cybersecurity threat detection, highlighting their respective advantages and limitations.

Findings

Random Forest and Extra Trees outperform others in accuracy.

Deep learning models like VGG16 show promise but have higher computational costs.

Model deployment faces challenges like data dependency and resource demands.

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.