A Machine Learning-Based Classification and Prediction Technique for DDoS Attacks

Abstract

Distributed network attacks are often referred to as Distributed Denial of Service (DDoS) attacks. These attacks take advantage of specific limitations that apply to any arrangement asset, such as the framework of the authorized organization's site. In the existing research study, the author worked on an old KDD dataset. It is necessary to work with the latest dataset to identify the current state of DDoS attacks. This paper, used a machine learning approach for DDoS attack types classification and prediction. For this purpose, used Random Forest and XGBoost classification algorithms. To access the research proposed a complete framework for DDoS attacks prediction. For the proposed work, THE UNWS-np-15 dataset from GitHub and python used as a simulator. After applying the machine learning models generated a confusion matrix for model performance identification. In the first classification, the results showed that both Precision (PR) and Recall (RE) are 89% for Random Forest Algorithm. The average Accuracy (AC) of model is 89% which is extremely good. In the second classification, the results showed that both Precision (PR) and Recall (RE) are 90% for XGBoost. The average Accuracy (AC) of model is 90%. By comparing work to existing research work, the accuracy of defect determination improved as compare to existing research work which is 85% and 79%.

DDoS attacks and machine‐learning‐based detection methods: A survey and taxonomy

“…Ismail et al 96 proposed a method for detecting, classification and prediction of DDoS attacks using machine learning. The authors use the UNSW‐NP 15 dataset to develop a framework for DDoS attack prediction, using the RF and XGBoost classification algorithms.…”

Proposed algorithm for smart grid DDoS detection based on deep learning

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IoTDevID: A Behavior-Based Device Identification Method for the IoT

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