| Title | Recommendation-based Security Model for Ubiquitous system using Deep learning Technique |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Agarkhed, Jayashree, Pawar, Geetha |
| Conference Name | 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS) |
| Keywords | Collaboration, Computational modeling, control systems, Deep Learning, feature extraction, Human Behavior, human factors, pubcrawl, Recommendation-based, recommender systems, resilience, Resiliency, Scalability, Sensitivity, trust models, ubiquitous computing, Unfair recommendation |
| Abstract | 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. |
| Notes | ISSN: 2768-5330 |
| DOI | 10.1109/ICICCS53718.2022.9788329 |
| Citation Key | agarkhed_recommendation-based_2022 |
Recommendation-based Security Model for Ubiquitous system using Deep learning Technique