Decision Tree Based Data Pruning with the Estimation of Oversampling Attributes for the Secure Communication in IOT


  • Nachaat Mohamed Rabdan Academy, UAE, Abu Dhabi, Homeland Security Universiti Sains Malaysia, Abu Dhabi
  • Aishwary Awasthi Research Scholar, Department of Mechanical, Sanskriti University, Mathura, Uttar Pradesh, India
  • Nandini Kulkarni Symbiosis School of Planning Architecture and Design, Symbiosis International Deemed University., Symbiosis School of Planning Architecture and Design, Nagpur
  • Sridhar Thota Professor, Department of ECE, Alliance College of Engineering and Design, Alliance University, Bangalore, India
  • Mandeep Singh Assistant Professor, Department of Physical Education, University of Jammu, Jammu
  • Sumedh Vithalrao Dhole Assistant Professor, Department of ECE, Bharati Vidyapeeth (Deemed to be University), College of Engineering,Pune-satara road, Dhankawadi, Pune, Maharashtra, India


Security, Attacks, Internet of Things (IoT), Pruning, Decision Tree, Oversampling Model


Internet of Things (IoT) exhibits a significant role to evaluate the error or supply shortage. The IoT demand for the security and authentication of the devices is considered as the most priority for software developments.  As the IoT communication comprises of an interconnected environment for the both digital and physical scenarios. The IoT environment exhibits anything and anywhere services to the communication medium. In those scenarios, security is considered as the major concern to protect the data resources from unauthorized resources for the appropriate security and privacy. This paper proposed a decision tree-based pruning scheme for the IoT attributes. The proposed decision tree based pruning for the security attributes are defined as the decision tree pruning (DTP). The proposed DTP model comprises of the minority oversampling model for the estimation of the attack features. With the developed DTP model, the attack datasets were pre-processed and evaluated for the different attack environments in to consideration. The DTP processed data were applied over the conventional machine learning-based model for the computation attacks in the network. The simulation results expressed that proposed DTP model achieves the accuracy value of 98% which is ~3% higher than the conventional classifier techniques.


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Process Flow in DTP




How to Cite

N. . Mohamed, Aishwary Awasthi, N. . Kulkarni, S. . Thota, M. . Singh, and S. . Vithalrao Dhole, “Decision Tree Based Data Pruning with the Estimation of Oversampling Attributes for the Secure Communication in IOT”, Int J Intell Syst Appl Eng, vol. 10, no. 2s, pp. 212 –, Dec. 2022.



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