Network Anomaly Detection Using a Hybrid Approach of Machine Learning Algorithms
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Keywords:
Network Security, Supervised Learning, Machine Learning, Metaheuristic Algorithms, KDD Cup 1999Abstract
Internet, while being of vital importance, has also brought along cyber attacks and threats.
Detection systems in cybersecurity have gained importance to counter these threats. Systems like network
anomaly detection can identify abnormal activities by learning normal network traffic. Hybrid models have
shown high success in cyber attack detection. In tests conducted on the KDD Cup 1999 dataset, machine
learning methods such as Decision Trees, Logistic Regression, Naive Bayes, Random Forest, and k-Nearest
Neighbors have exhibited high accuracy levels. Two different hybrid feature selection methods, PCA +
RFECV and RFECV + FS, were compared, and it was observed that feature selection plays a critical role
in classification performance. These methods can enhance classification performance by reducing the
dimensionality of the dataset and selecting meaningful features. This study emphasizes the importance of
cybersecurity detection systems in minimizing the potential damage of digital attacks while safeguarding
the information of individuals and organizations.
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