We have only taken the three most relevant features from the dataset by combining two results from previous feature selection work which used Support Vector Machines (SVMs) and Information Gain. This dataset comprises of a large amount of normal and attack data each of which contains 41 features. In addition to fuzzy logic, the prediction model was developed and evaluated based on data provided by NSL-KDD 99 dataset for the use of overall design. Fuzzy logic provides a powerful tool for decision making to handle imprecise data. Since the prediction process contains uncertainty, there is no exact and mathematical solution to solve the problem. In this paper, we will propose an approach to predict the occurrence of denial of service against the availability of the servers or the target machines in order to prevent them from being disrupted to provide services.
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Prediction model towards DoS attack, on the contrary, has not been sufficiently addressed in the literature. Most of existing model focused on DoS attack detection and response.
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