Performance Comparison of Feature Selection Methods for Detecting Trojan Activity
Main Article Content
- Viruses are malicious programs that can be harmful. One of the most dangerous viruses is the trojan virus, where the trojan virus hides on the user's device without being aware of its existence. Trojan viruses can be very difficult to spot because they hide on network devices and disguise themselves as part of the device. However, when a network device is infected by a trojan virus attack, the activities that occur on the network will be different from usual activities. In network activity, there are various parameters that cause classification to take longer to predict. In this study, various comparisons of feature reduction algorithms between Coefficient Correlation, Information Gain, PCA, and LDA were carried out and tested the combination of classification model algorithms (Random Forest, Decision Tree, KNN, Naïve Bayes, AdaBoost) to detect the best trojan activity on the internet network. faster to increase security against trojan viruses. The results of the study show that the classification with maximum accuracy with the best time is obtained by a combination of Coefficient Correlation, Information Gain, and PCA using the Decision Tree classification, using a combination of feature selection and classification methods obtained 99% accuracy and prediction time of 0.0033 seconds.
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