scholarly journals Intrusion Detection System Using Support Vector Machine and Decision Tree

2010 ◽  
Vol 3 (3) ◽  
pp. 40-43 ◽  
Author(s):  
Snehal A. Mulay ◽  
P.R. Devale ◽  
G.V. Garje
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Intrusion Detection System ◽  
Detection System ◽  
Support Vector

scholarly journals Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 173 ◽  
Author(s):  
Ansam Khraisat ◽  
Iqbal Gondal ◽  
Peter Vamplew ◽  
Joarder Kamruzzaman ◽  
Ammar Alazab
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Decision Tree ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
False Alarm Rates

Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates.

Search on Method of Netwok Intrusion Detection Based on Support Vector Machine with Pre-Extracting Support Vector

2013 ◽  
Vol 655-657 ◽  
pp. 1787-1790
Author(s):  
Sheng Chen Yu ◽  
Li Min Sun ◽  
Yang Xue ◽  
Hui Guo ◽  
Xiao Ju Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Algorithm ◽  
Support Vector ◽  
Distance Ratio ◽  
Training Time ◽  
Training Examples ◽  
Center Distance

Intrusion detection algorithm based on support vector machine with pre-extracting support vector is proposed which combines the center distance ratio and classification algorithm. Given proper thresholds, we can use the support vector as a substitute for the training examples. Then the scale of dataset is decreased and the performance of support vector machine is improved in the detection rate and the training time. The experiment result has shown that the intrusion detection system(IDS) based on support vector machine with pre-extracting support needs less training time under the same detection performance condition.

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