A Cloud Based Machine Intelligent Framework to Identify DDoS Botnet Attack in Internet of Things
Keywords:
Security, DDoS Botnet Attack, IoTs, Supervised Machine Learning, Classification AccuracyAbstract
First few Botnet attack is a major issue in security of Internet of Things (IoT) devices and it needs to be identified to secure the system from the attackers. In this paper, a cloud-based machine intelligent framework is proposed to identify DDoS (distributed denial of service) Botnet attack in IoT systems. In this framework, the IoT devices communicating with the cloud are categorized based on their communication record to check the DDoS Botnet attack. In this work, three DDoS botnet attacks are considered such as HTTP, UDP, and TCP. The cloud is installed with a supervised machine intelligent model to classify the type of DDoS attack. The model is selected by considering 4 models such as Tree, stacking classifier, Neural Network (NN), and Support Vector Machine (SVM) and performance is evaluated based on classification accuracy (CA). Here, the stacking classifier is a hybrid model designed using the aggregation of Logistic Regression (LR), NN, and SVM. The performance is evaluated using Python tool. From the results it was found that except Tree all three models show an CA of 1.0. The computation time is also analyzed for four models and it was found that Tree shows less time than other models however, if considered with respect to CA (1.0) then SVM can be preferred as it shows lesser time then other two models. The detection time of the IoT devices is also simulated and result show that if SVM is installed in cloud then the detection time is lesser.
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References
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