Enhanced Security Model for Pervasive Computing Using Machine Learning Techniques

In recent mobile world the pervasive computing plays the vital role in data computing and communication. The pervasive computing provides the mobile environment for decentralized computational services where the user work and socializes. Pervasive computing in recent trend moves away from the desktop to make surrounding as flexible and portable dev ices like laptop, notepad, smartphones and personal digital assistants. Pervasive environment devices are worldwide and able to receive various communication services including TV, cable network, radio station and other audio-visual services. The users and the system in this pervasive environment may face the challenges of user trust, data privacy and user and device node identity. To give the feasible determination for these challenges. This paper aims to propose a dynamiclearning pervasive computing environment to refer the challenges’ proposed efficient trust model (ETM) for trustworthy and untrustworthy attackers. ETM model also compared with existing generic models, it also provides 97 % accuracy rate than existing models.