Biblio

Ubiquitous environment embedded with artificial intelligent consist of heterogenous smart devices communicating each other in several context for the computation of requirements. In such environment the trust among the smart users have taken as the challenge to provide the secure environment during the communication in the ubiquitous region. To provide the secure trusted environment for the users of ubiquitous system proposed approach aims to extract behavior of smart invisible entities by retrieving their behavior of communication in the network and applying the recommendation-based filters using Deep learning (RBF-DL). The proposed model adopts deep learning-based classifier to classify the unfair recommendation with fair ones to have a trustworthy ubiquitous system. The capability of proposed model is analyzed and validated by considering different attacks and additional feature of instances in comparison with generic recommendation systems.

In new technological world pervasive computing plays the important role in data computing and communication. The pervasive computing provides the mobile environment for decentralized computational services at anywhere, anytime at any context and location. Pervasive computing is flexible and makes portable devices and computing surrounded us as part of our daily life. Devices like Laptop, Smartphones, PDAs, and any other portable devices can constitute the pervasive environment. These devices in pervasive environments are worldwide and can receive various communications including audio visual services. The users and the system in this pervasive environment 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 dynamic learning in pervasive computing environment refer the challenges proposed efficient security model (ESM) for trustworthy and untrustworthy attackers. ESM model also compared with existing generic models; it also provides better accuracy rate than existing models.