Abstract
Network security has become an important aspect in terms of confidentiality and integrity. To protect our system from these internet attacks, without any compromise on the security constraints, we have developed a system using the combination of two soft computing techniques, namely fuzzy and neural network. The designed system for intrusion detection is the Adaptive Neuro-Fuzzy Inference System (ANFIS), which detects whether the incoming data is normal or an attack. To train the system, we have used KDD dataset and to evaluate the performance parameters based on the confusion matrix generated. For the system to work with high accuracy, the True Negative Rate and True Positive Rate must be maximum. This paper compares the fuzzy and neural network techniques (developed previously) using the same dataset with that of neuro fuzzy. The paper mainly focuses on ANFIS and the concepts of fuzzy and neural network used to develop this system.
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Dixit, M., Ukarande, R. (2018). Internet Traffic Intrusion Detection System Using Adaptive Neuro-Fuzzy Inference System. In: Deshpande, A., et al. Smart Trends in Information Technology and Computer Communications. SmartCom 2017. Communications in Computer and Information Science, vol 876. Springer, Singapore. https://doi.org/10.1007/978-981-13-1423-0_3
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DOI: https://doi.org/10.1007/978-981-13-1423-0_3
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