Efficient Trust Inference Model for Pervasive Computing Based on Hybrid Deep Learning

Authors

  • Geetha Pawar Assistant Professor, Department of Computer Science and Engineering, Rajiv Gandhi Institute of Technology, Bengaluru, Karnataka, India.
  • Jayashree Agarkhed Professor, Department of Computer Science and Engineering, Poojya Doddappa Appa College of Engineering, Gulbarga, Karnataka, India.

Keywords:

Anomaly Detection, Machine Learning, Security, Pervasive Computing, Enhanced Trust model

Abstract

With the rise of mobile technology, pervasive computing has become an indispensable tool for processing and exchanging data in today’s connected society. For distributed computing services to be used in the same places where people live and socialise, pervasive computing must be present. Recent developments in pervasive computing have shifted the focus from stationary computers to mobile ones, such as laptops, notepads, cell phones, and PDAs. Devices in the pervasive environment are available on a global scale and are capable of receiving a wide range of audio-visual as well as other telecommunications services. Problems of user trust, data protection, and client and device node identification may arise for the system and users in this extensive setting. In this study, we proposed an efficient trust inference model for ubiquitous computing associated fine-tuned ANN and ML IoT attack predictor which reached an accuracy of 90.43%.

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Security Issues in Ubiquitous Computing

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Published

17.02.2023

How to Cite

[1]
G. . Pawar and J. . Agarkhed, “Efficient Trust Inference Model for Pervasive Computing Based on Hybrid Deep Learning”, Int J Intell Syst Appl Eng, vol. 11, no. 2, pp. 170–179, Feb. 2023.

Issue

Vol. 11 No. 2 (2023): (Article in Press)

Section

Research Article

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