| dc.description.abstract |
The use of anomaly-based classification of intrusions has increased
significantly for Intrusion Detection Systems. Large
number of training data samples and a good ‘feature set’
are two primary requirements to build effective classification
models with machine learning algorithms. Since the amount
of data available for malicious traffic will often be small
compared to the available traces of benign traffic, extraction
of ‘good’ features which enable detection of malicious traffic
is a challenging area of work.
This research work presents preliminary results of comparison
of performance of three different feature selection
algorithms - Correlation based feature selection, Consistency
based subset evaluation and Principal component analysison
three different Machine learning techniques- namely Decision
trees, Na¨ıve Bayes classifier, and Bayesian Network
classifier. These algorithms are evaluated for the detection
of Peer-to-Peer (P2P) based botnet traffic. |
en_US |