A Cloud Based Machine Intelligent Framework to Identify DDoS Botnet Attack in Internet of Things

Authors

  • Sourav Kumar Bhoi Post Doctoral Fellow, Research Center Department, Computer Science and Information Science, Institute of Computer Science and Information Science, Srinivas University, Mangaluru-575001, Karnataka, India Author
  • Prasad K Krishna Associate Professor, Institute of Computer Science and Information Science, Srinivas University, Pandeshwar, Mangaluru, Karnataka, India Author

Keywords:

Security, DDoS Botnet Attack, IoTs, Supervised Machine Learning, Classification Accuracy

Abstract

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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Published

2022-08-30

Issue

Vol. 9 No. 4 (2022): International Journal of Innovative Research in Engineering & Management (August) 2022

Section

Articles

How to Cite

A Cloud Based Machine Intelligent Framework to Identify DDoS Botnet Attack in Internet of Things . (2022). International Journal of Innovative Research in Engineering & Management, 9(4), 1–5. Retrieved from https://acspublisher.com/journals/index.php/ijirem/article/view/10775