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Intrusion Detection System Using Principal Component Analysis with Random Forest Approach

Abstract: Through the evolution in radio communication, there are many security threats over the internet. The intrusion detection system helps to find the outbreaks on the system and the intruders are detected. Previously various machine learning techniques are applied on the IDS and tried to progress the results on the detection of trespassers and to increase the precision of the IDS. This paper has expected for an approach to develop IDS by using the principal component analysis (PCA) and the random forest classification algorithm. Where the PCA will help to shape the dataset by reducing the dimensionality of the dataset and the random forest will help in classification. Results obtained states that the proposed approach works more resourcefully in terms of accuracy as compared to other methods like SVM, Naïve Bayes, and Decision Tree. The results obtained by projected method are having the values for performance time (min) is 3.24 minutes, Accuracy rate (%) is 96.78 %, and therefore the Error rate (%) is 0.21%. Keywords: Intrusion detection, Principal component Analysis, and Random Forest.