The Role of Data Mining in Cybersecurity: An Overview of Techniques and Challenges
Main Article Content
Abstract
Big data mining is a process utilized to find hidden insights and patterns in large datasets. It can be used in various fields, such as healthcare, social sciences, and business. One of its applications in cybersecurity is analyzing network traffic to identify potential threats. The increasing volume of network traffic has led to the development of new techniques for analyzing and detecting cyber threats. These include the use of statistical techniques such as SVMs and Naive Bayes, as well as random forests. Traditional IDS systems are no longer able to identify complex attacks. This project aims to analyze the data collected from the NSL-KPDD dataset using different machine learning methods. Some of these include SVM with linear, RBF kernel, RVM with a polynomial, and Naive bayes. The performance of these methods is evaluated according to their accuracy, recall, F1-score, and precision. The results of a study revealed that SVM with the RBF kernel performed better than the other algorithms when it came to detecting network intrusions. It also outperformed Random Forests. The findings suggest that this algorithm could be useful in identifying network threats.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.