Manish C. Sabraj

Work place: Shri Mata Vaishno Devi University / Electronics and Communication Engineering, Katra, 182320, India

E-mail: manish.sabraj@gmail.com

Website:

Research Interests: Wireless Sensor Networks

Biography

Dr. Manish Sabraj is currently an Assistant Professor in the School of Electronics and Communication Engineering, Shri Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India. He has received his B.Tech degree in Electronics and Communication from Govt. College of Engineering and Technology Jammu and Kashmir, India, in 2001 and M.Tech degree in Electronics and Communication from IIT Guwahati, Guwahati, India, in 2003. He has completed his Ph.D. degree from Shri Mata Vaishno Devi University, Jammu and Kashmir, India in 2013. His research interest includes Digital Signal Processing, Wireless Sensor Networks, and Spectrum estimation and analysis.

Author Articles
Attack Modeling and Security Analysis Using Machine Learning Algorithms Enabled with Augmented Reality and Virtual Reality

By Momina Mushtaq Rakesh Kumar Jha Manish C. Sabraj Shubha Jain

DOI: https://doi.org/10.5815/ijcnis.2024.04.08, Pub. Date: 8 Aug. 2024

Augmented Reality (AR) and Virtual Reality (VR) are innovative technologies that are experiencing a widespread recognition. These technologies possess the capability to transform and redefine our interactions with the surrounding environment. However, as these technologies spread, they also introduce new security challenges. In this paper, we discuss the security challenges posed by Augmented reality and Virtual Reality, and propose a Machine Learning-based approach to address these challenges. We also discuss how Machine Learning can be used to detect and prevent attacks in Augmented reality and Virtual Reality. By leveraging the power of Machine Learning algorithms, we aim to bolster the security defences of Augmented reality and Virtual Reality systems. To accomplish this, we have conducted a comprehensive evaluation of various Machine Learning algorithms, meticulously analysing their performance and efficacy in enhancing security. Our results show that Machine Learning can be an effective way to improve the security of Augmented reality and virtual reality systems.

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