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The Classification of DDoS Attacks Using Learning Techniques : Distributed Denial of Service (DDoS) is a well-known attack with the power of damage. The process of DDoS is to disrupt the normal traffic of a targeted server by overwhelming it with a flood of Internet traffic. This action affects the legitimate users inaccessible to the resources. Moreover, many businesses today have been faced with DDoS attacks derogation that not only take the physical damage of resources, but also cause a massive financial damage. The idea of this research is to find good ways to detect and classify DDoS attacks that aim to avoid the cause of failure due to network attacks by using deep learning (DL) techniques. Therefore, the proposed models based on deep neural networks have been provided to perform the capability in the multiclass classification of DDoS. CICDDoS2019 is a new taxonomy of the DDoS attacks dataset that has been utilized as the reference of this framework. Two proposed models have been implemented with the simple DNN structure and the Convolutional autoencoder. Overall accuracies obtained from the proposed models are 0.812 and 0.851, respectively. Furthermore, the correct prediction of each class is high up to 0.999. The results showed that the proposed networks display the satisfying outcome with the high accuracy, precision, recall, and F1-score. The comparisons of the proposed models with the reference network and other machine learning algorithms, namely, Logistic Regression, and Naïve Bayes are also indicated in this research and th
ผู้แต่ง
Jirasin Boonchai
ประเภทสิ่งพิมพ์
วิทยานิพนธ์ (MIT) 2021
เลขหน้า
74
หัวเรื่อง
Information Technology--Thesis
หัวเรื่อง
Thai-Nichi Institute of Technology.Information Technology--Thesis
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