Multilevel Intrusion Detection System with Affinity Clustering and Ensemble SVM
DOI:
https://doi.org/10.32628/CSEIT206431Keywords:
Intrusion Detection System, Affinity Clustering, Ensemble Support Vector Machine, NSL-KDD, UNSW-NB 15, Detection RateAbstract
Now-a-days with growing applications over internet increases the security issues over network. Many security applications are designed to cope with such security concerns but still it required more attention to improve speed as well accuracy. With advancement of technologies there is also evolution of new threats or attacks in network. So, it is required to design such detection system that can handle new threats in network. One of the network security tools is intrusion detection system which is used to detect malicious data packets. Machine learning tool is also used to improve efficiency of network-based intrusion detection system. In this paper, an intrusion detection system is proposed with an application of machine learning tools. The proposed model integrates feature reduction, affinity clustering and multilevel Ensemble Support Vector Machine. The proposed model performance is analyzed over two datasets i.e. NSL-KDD and UNSW-NB 15 dataset and achieved approx. 12% of efficiency over other existing work.
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