The obtained results compared with well-known studies presented in this field.
📌 3 Key Takeaways
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Objective & Hypothesis:Öz This study presents new method to detect DDOS attacks by using Deep Belief Networks (DBN).
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Key Findings & Data:The input data which represented the DDoS features in cloud environment are first analyzed by using DBN to extracted high level and sensitive features.
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Conclusion & Impact:The obtained results compared with well-known studies presented in this field.
Öz This study presents new method to detect DDOS attacks by using Deep Belief Networks (DBN). The input data which represented the DDoS features in cloud environment are first analyzed by using DBN to extracted high level and sensitive features. The output of the DBN wired to the classifier (SoftMax and SVM). The aim of using the DBN is to extracted features that have ability to present the best classification results and to speed up the processing time by reducing the dimension of features. In the last stage, the Classifier trained in supervised method to classify the features into two labels there is attack or not. The obtained results compared with well-known studies presented in this field.
IBRAHİM, I., KURNAZ, S. (2021). A NEW DISTRIBUTED DENIAL-OF-SERVICE DETECTION SYSTEM IN CLOUD ENVIRONMENT BY USING DEEP BELIEF NETWORKS. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, Cilt: 63 - Sayı: 1, 17–24. https://ajindex.com/articles/105160
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