Evaluating Machine Learning Classifiers in Detecting Cyberattacks

This study aims to develop a machine learning model that can accurately detect cyberattacks. We compare the performance of Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF) in predicting cyberattacks. Furthermore, we investigate whether using Information Gain Attribute Evaluation (IGAE) for feature selection improves the performance of the algorithms. This work provides a clear comparison of the algorithms and shows the most suitable one for classifying cyberattacks. In addition, this study combines LR and RF using a voting classifier along with IGAE and compares its performance with that of the rest of the algorithms. We investigate whether combining algorithms increases the accuracy of the results. The results show that the most accurate algorithm is RF, followed by LR and SVM. Contrary to initial expectations, the findings further indicate that the application of IGAE marginally reduces algorithm accuracy across the tested classifiers, suggesting that feature selection through information gain is not universally beneficial in cyberattack detection tasks. These findings contribute to the growing body of knowledge on effective machine learning methodologies for cybersecurity applications.