Efficient classification using parallel and scalable compressed model and its application on intrusion detection
We propose a compressed model composed of horizontal and vertical compression. We employ OneR as horizontal compression, AP clustering as vertical compression. We implement a Map-Reduce based scalable and parallel framework for compression. We use KNN and SVM to build intrusion detector using the proposed compressed model. Both KDD99 and CDMC2012 are evaluated to show the detection efficiency and accuracy.
classification parallelization compressed model intrusion detection mapreduce