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
واثق لفته عبدعلي طه الياسين | Wathiq Laftah Al-Yaseen | 3764
نوع النشر
مؤتمر علمي
المؤلفون بالعربي
Wathiq Laftah Al-Yaseen, Zulaiha Ali Othman, and Mohd Zakree Ahmad Nazri
المؤلفون بالإنجليزي
Wathiq Laftah Al-Yaseen, Zulaiha Ali Othman, and Mohd Zakree Ahmad Nazri
الملخص العربي
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%.
الملخص الانجليزي
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%.
تاريخ النشر
27/08/2015
الناشر
Springer
رقم المجلد
رقم العدد
رابط DOI
10.1007/978-981-287-936-3_25
الصفحات
265–274
رابط الملف
تحميل (133 مرات التحميل)
الكلمات المفتاحية
intrusion detection system, network security, support vector machine, K-means, multi-level SVM.
رجوع