One of the major challenges that faces the acceptance and growth rate of business and governmental sites is a Botnet-based DDoS attack. A flooding DDoS strikes a victim machine by means of sending a vast amount of malicious traffic, causing a significant drop in the service quality (QoS) in IoT devices. Nonetheless, it is not that easy to detect and tackle flooding DDoS attacks, owing to the significant number of attacking machines, the usage of source-address spoofing, and the common areas shared between legitimate and malicious traffic. New kinds of attacks are identified daily, and some remain undiscovered, accordingly, this paper aims to improve the traffic classification algorithm of network traffic, that hackers use to try to be ambiguous or misleading. A recorded simulated traffic was used for both samples; normal and DDoS attack traffic, approximately 104.000 cases of each, where both datasets -which were created for this study- represent the input data in order to create a classification model, to be used as a tool to mitigate the risk of being attacked.
The next step is putting datasets in a format suitable for classification. This process is done through preprocessing techniques, to convert categorical data into numerical data. A classification process is applied to capture datasets, to create a classification model, by using five classification algorithms which are; Decision Tree, Support Vector Machine, Naive Bayes, K-Neighbours and Random Forest. The core code used for classification is the python code, which is controlled by a user interface. The highest prediction, precision and accuracy are obtained using the Decision Tree and Random Forest classification algorithms, which also have the lowest processing time.
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