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International Journal of

Recent Trends in Engineering

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International Journal of Recent Trends in Engineering (IJRTE)

ISSN 1797-9617

Volume 1,† Number 1,† May 2009

Issue on Computer Science

Page(s): 472-477

Evaluating Machine Learning Algorithms For Detecting Network Intrusions

†††††††††† Mrutyunjaya Panda and Manas Ranjan Patra

Full text:† PDF


With recent advances in network based technology and increased dependability of our everyday life on this technology, assuring reliable operation of network based system is very important. Signature based intrusion detection systems cannot detect new attacks. These systems are the most used and developed ones. Current anomaly based intrusion detection systems are also unable to detect all kinds of new attacks because they are designed to restricted applications on limited environments. It is important problems to increase the detection rates and reduce the false positive rates in network intrusion detection systems (NIDS). In this paper, we propose machine learning algorithms such as Random Forest and AdaBoost, along with NaÔve Bayes, to build an efficient intrusion detection model. We also report our experimental results over KDDCupí1999 datasets. The results shows that the choice of any data mining algorithm is a compromise among the time taken to build the model, detection rate and low false alarm rate .

Index Terms

Intrusion Detection, Machine Learning algorithms, Precision-Recall Characteristics, ROC, Cost Matrix

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