Predicting unlabeled traffic for intrusion detection using semi-supervised machine learning
Anku Jaiswal, A S Manjunatha, B R Madhu, Murthy P. Chidananda · 2016
Intrusion is one of the most serious problems with network Security, as new types of intrusions are getting much more challenging to detect. Large amount of network traffic has been generated due to the use of internet; most of the generated traffic is in the format which cannot be used directly to arrive at meaningful information. The cleansing and labeling of data each time needs a considerable amount of human effort, and is time consuming. In this paper we show how, Semi supervised machine learning technique can be used in intrusion detection, for both labeled and unlabeled data. In the proposed technique we take a small amount of labeled data to create model and using this model we show how to predict the unlabeled traffic. Machine Learning tool is used for this purpose which uses semi-supervised classifier to build the model. The created model is then integrated in Pentaho which with the help of Weka Scoring provides the expected output. The proposed technique helps the network administrator to take quick decision by classifying the incoming traffic as either malicious or normal and hence efficient detection of intrusion.