A comparative analysis of SVM and its stacking with other classification algorithm for intrusion detection

Network attacks have become more pervasive in the cyber world. There are various attacks such as denial of service, scanning, privilege escalation that is increasing day by day leading towards the requirement of a more robust and adaptable security techniques. Anomaly detection is the main focus of...

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Bibliographic Details
Published in2016 International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Spring) pp. 1 - 6
Main Authors Chand, Nanak, Mishra, Preeti, Krishna, C. Rama, Pilli, Emmanuel Shubhakar, Govil, Mahesh Chandra
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2016
Subjects
Algorithm design and analysis
Data mining
Intrusion detection
Machine Learning
Machine learning algorithms
Multi-Classifier
Support vector machines
Training
Vegetation
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DOI10.1109/ICACCA.2016.7578859

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Summary:Network attacks have become more pervasive in the cyber world. There are various attacks such as denial of service, scanning, privilege escalation that is increasing day by day leading towards the requirement of a more robust and adaptable security techniques. Anomaly detection is the main focus of our paper. Support Vector Machine (SVM) is one of the good classification algorithm applied specially for intrusion detection. However, its performance can be significantly improved when it is applied in integration with other classifiers. In this paper, we have performed a comparative analysis of SVM classifier's performance when it is stacked with other classifiers like BayesNet, AdaBoost, Logistic, IBK, J48, RandomForest, JRip, OneR and SimpleCart. Multi-Classifier algorithm have better classification power when compared to a single classifier algorithm specially for detecting low frequency attacks such as guess password, rootkits, spyware etc. Our preliminary analysis over NSL-KDD'99 dataset shows that stacking of SVM and Random Forest provides the best performance with accuracy of around 97.50% which apparently better than SVM (91.81%).
DOI:10.1109/ICACCA.2016.7578859