عنوان المقالة:Intrusion Detection System Based on Modified K-means and Multi-level Support Vector Machines Intrusion Detection System Based on Modified K-means and Multi-level Support Vector Machines
Wathiq Laftah Al-Yaseen, Zulaiha Ali Othman, and Mohd Zakree Ahmad Nazri
English Authors
Wathiq Laftah Al-Yaseen, Zulaiha Ali Othman, and Mohd Zakree Ahmad Nazri
Abstract
This paper proposed a multi-level model for intrusion detection that
combines the two techniques of modified K-means and support vector machine
(SVM). Modified K-means is used to reduce the number of instances in a training
data set and to construct new training data sets with high-quality instances.
The new, high-quality training data sets are then utilized to train SVM classifiers.
Consequently, the multi-level SVMs are employed to classify the testing
data sets with high performance. The well-known KDD Cup 1999 data set is
used to evaluate the proposed system; 10% KDD is applied for training, and
corrected KDD is utilized intesting. The experiments demonstrate that the proposed
model effectively detects attacks in the DoS, R2L, and U2R categories. It
also exhibits a maximum overall accuracy of 95.71%.
Abstract
This paper proposed a multi-level model for intrusion detection that
combines the two techniques of modified K-means and support vector machine
(SVM). Modified K-means is used to reduce the number of instances in a training
data set and to construct new training data sets with high-quality instances.
The new, high-quality training data sets are then utilized to train SVM classifiers.
Consequently, the multi-level SVMs are employed to classify the testing
data sets with high performance. The well-known KDD Cup 1999 data set is
used to evaluate the proposed system; 10% KDD is applied for training, and
corrected KDD is utilized intesting. The experiments demonstrate that the proposed
model effectively detects attacks in the DoS, R2L, and U2R categories. It
also exhibits a maximum overall accuracy of 95.71%.