Efficient classification using parallel and scalable compressed model and Its application on intrusion detection
This work addresses efficiency in intrusion detection for cybersecurity applications, presenting an incremental improvement through parallelization and compression techniques.
The paper tackles the problem of slow classification in intrusion detection by proposing a compressed model that combines horizontal and vertical compression, achieving up to 184 times speedup with less than 1% average accuracy loss on KDD99 and CMDC2012 datasets.
In order to achieve high efficiency of classification in intrusion detection, a compressed model is proposed in this paper which combines horizontal compression with vertical compression. OneR is utilized as horizontal com-pression for attribute reduction, and affinity propagation is employed as vertical compression to select small representative exemplars from large training data. As to be able to computationally compress the larger volume of training data with scalability, MapReduce based parallelization approach is then implemented and evaluated for each step of the model compression process abovementioned, on which common but efficient classification methods can be directly used. Experimental application study on two publicly available datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the classification using the compressed model proposed can effectively speed up the detection procedure at up to 184 times, most importantly at the cost of a minimal accuracy difference with less than 1% on average.