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Detecting the denial of service attacks that solely target the router is a maximum security imperative in deploying IPv6 networks. The state-of-the-art Denial of Service detection methods aim at leveraging the advantages of flow statistical features and machine learning techniques. However, the detection performance is highly affected by the quality of the feature selector and the reliability of datasets of IPv6 flow information. This paper proposes a new neuro-fuzzy inference system to tackledoi:10.34028/iajit/17/1/3 fatcat:7haqql3jlzfrtckeoatmuxi4m4