A Novel Fuzzy Min-Max Neural Network and Genetic Algorithm-Based Intrusion Detection System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 380)


Today in the era of ICT, security of data and services on the WWW has become the most important issue for web service providers. Loopholes in the security systems of WWW may break the integrity, reliability, and availability of data and services. Today, intrusion detection systems based on data mining is the best security framework for the Internet. In this paper a novel intrusion detection system is proposed which is based on the fuzzy min-max neural network and the genetic algorithm. The proposed model is trained using fuzzy min-max neural network and the learning system is optimized by application of genetic algorithm. The developed system is tested on the KDD Cup dataset. The parameters classification accuracy and classification error were used as a final performance evaluator of the learning process. The experimental results show that the proposed model gives superior performance over other existing frameworks.


IDS Anomaly detection Misuse detection FMM NN Genetic algorithm 


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Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringBirla Institute of TechnologyMesraIndia

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