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

• URL: http://arxiv.org/abs/2407.06014v1
• Date: Mon, 8 Jul 2024 15:05:59 GMT
• Title: Evaluating Predictive Models in Cybersecurity: A Comparative Analysis of Machine and Deep Learning Techniques for Threat Detection
• Authors: Momen Hesham, Mohamed Essam, Mohamed Bahaa, Ahmed Mohamed, Mohamed Gomaa, Mena Hany, Wael Elsersy,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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