Intrusion Detection and Forensics based on decision tree and Association rule mining for Probe attack detection
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2015, Vol 5, Issue 4
Abstract
This paper present an approach based on the combination of, two techniques using decision tree and Association rule mining for Probe attack detection. This approach proves to be better than the traditional approach of generating rules for fuzzy expert system by clustering methods. Association rule mining for selecting the best attributes together and decision tree for identifying the best parameters together to create the rules for fuzzy expert system. After that rules for fuzzy expert system are generated using association rule mining and decision trees. Decision trees is generated for dataset and to find the basic parameters for creating the membership functions of fuzzy inference system. Membership functions are generated for the probe attack. Based on these rules we have created the fuzzy inference system that is used as an input to neuro-fuzzy system. Fuzzy inference system is loaded to neuro-fuzzy toolbox as an input and the final ANFIS structure is generated for outcome of neuro-fuzzy approach. The experiments and evaluations of the proposed method were done with NSL-KDD intrusion detection dataset. As the experimental results, the proposed approach based on the combination of, two techniques using decision tree and Association rule mining efficiently detected probe attacks. Exp
Authors and Affiliations
Harishchandra Maurya , Swati Sharma
Keywords
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