The Efficiency of Machine Learning Techniques in Strengthening Defenses Against DDoS Attacks, Such as Random Forest, Logistic Regression, and Neural Networks
DOI:
10.33395/sinkron.v9i1.14502Keywords:
Machine learning, cybersecurity, DDoS detection, Random Forest, Logistic Regression, Neural NetworkAbstract
Distributed Denial of Service (DDoS) attacks are one of the most common cybersecurity concerns brought on by the quick development of digital technology. By flooding servers with too many requests, these assaults interfere with online services, highlighting the necessity of strong detection systems. Using the well-known CIC-DDoS2019 dataset, this study explores the use of machine learning algorithms—Random Forest (RF), Logistic Regression (LR), and Neural Networks (NN)—to improve DDoS assault detection. A comprehensive preprocessing procedure that comprised feature selection, normalization, and duplication removal was applied to dataset in order to ensuring optimal algorithm performance. With an accuracy of 97% on the entire test dataset and 99.13% on the training and validation datasets, RF showed exceptional performance. While NN successfully managed intricate data patterns, attaining an accuracy of roughly 94%, LR demonstrated impressive results with an accuracy of 98.65%. Because of its ensemble method, which minimizes overfitting and improves model generalization, the RF algorithm performed better than the others. This study highlights how machine learning may be used to solve practical cybersecurity issues by offering insightful information about how to optimize algorithms for real-time DDoS detection. The results improve the stability and resilience of digital infrastructures by aiding in the creation of effective intrusion detection systems. Future research can explore integrating advanced neural network architectures and hybrid methods to further improve detection rates and adaptability to evolving cyber threats.
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Copyright (c) 2025 Syauqii Fayyadh Hilal Z, Rushendra
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